#project-based Training in Embedded Linux with ARM

[emblogicsblog]

Embedded Linux ARM

Embedded Linux: The Heart of Modern Devices Embedded Linux is a lightweight version of the Linux operating system, optimized for embedded systems- specially designed computing devices whose purpose is to be integrated into any hardware. Embedded Linux has been widely used in IoT devices, automotive systems, industrial automation, and more in the application mainly because of its elasticity and efficiency.

Embedded Linux is designed to run effectively within constrained resource environments unlike traditional operating systems. Another key advantage of Embedded Linux is that developers can customize the kernel and software stack according to specific hardware requirements. Its scalability and robust community support give access to a large number of libraries and tools, reducing development time and cost. Moreover, because of its real-time processing capabilities and multiple communication protocol support, it has received immense importance in robotics, medical appliances, and smart appliances. Through its Embedded Linux course, Emblogic offers comprehensive training to meet the ever-increasing demand for skilled professionals in this field. This hands-on program equips participants with the knowledge and skills required to work with Embedded Linux systems, covering essential topics like kernel programming, device drivers, and system optimization. Emblogic has designed its curriculum to be industry-aligned, ensuring students are adequately equipped to face real-world challenges in this exciting domain. Therefore, for fresh professionals, Embedded Linux and its training at Emblogic lead to richer career opportunities in developing embedded systems.

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[technoscoe]

Top 10 Skills You’ll Learn in an Embedded System Development Course in India

Today, with advanced technology in every field, the world has taken a big step toward creating new industries and innovations. It is one of the most challenging and exciting fields, and it's worth investing in by enrolling in an embedded system development course in India. The knowledge and skills gained are useful for outstanding performance in various domains such as IoT, robotics, and automotive technology. Here, we look at the top 10 skills you would learn in an embedded system development course, including a fascinating project initiative, TechnosCOE.

1. Familiarity with Microcontrollers and Microprocessors

Microcontrollers and microprocessors are the foundation base for embedded systems. Courses include architecture, functioning, and programming, with hands-on experience in popular controllers such as Arduino, PIC, and ARM, which form the backbone of most embedded applications.

2. Programming Languages

One of the main emphases of an embedded system development course in India is acquiring skills in programming languages such as C and C++. These skills are essential to writing firmware and developing applications for embedded systems. It also makes some courses introduce Python for scripting and debugging purposes to improve a student's versatility.

3. Real-Time Operating Systems (RTOS)

The creation of efficient and reliable systems is based on the understanding of how RTOS works. These courses cover the principles of multitasking, scheduling, and inter-process communication. By mastering RTOS concepts, students can develop systems for industries such as telecommunications and healthcare.

4. Circuit Design and PCB Development

These contain custom circuitry designs and a printed circuit board (PCB). The knowledge gained from developing circuitry robust and efficient within Eagle and Altium Designer gives immense value toward the prototyping and product development phase.

5. Sensor integration and data acquisition

Modern embedded systems interact with the physical world through sensors. Courses teach students how to integrate sensors, process their data, and use it in meaningful ways. Applications include temperature monitoring, motion detection, and environmental sensing, among others.

6. IoT (Internet of Things) Development

IoT has changed the face of industries, and at the center of this change is the concept of embedded systems. Students are taught to design devices that are internet-enabled, which can talk to other devices, and perform analytics in real-time data. The same skill can be applied to smart home automation and industrial applications.

7. Embedded Linux

Training on Embedded Linux is generally a part of an embedded system development course in India. It is a highly versatile and widely used open-source software in the world of embedded systems. A student learns how to develop applications, configure the kernel, and build custom distributions for different types of devices.

8. Debugging and Testing Techniques

Debugging is a key tool in embedded system development. Students become experts in using tools like JTAG debuggers and oscilloscopes to identify and debug those issues. Techniques on testing address all the requirements for the performance and safety of the system.

9. Communication Protocols

Understanding communication protocols is very important to the embedded engineers. The curriculum covers some popular protocols such as I2C, SPI, UART, CAN, and Ethernet, which are usually used in applications such as car systems and automation in industrial places.

10. Project Management and Documentation

Beyond technical skills, students also learn project management techniques and documentation practices. These soft skills ensure that they can efficiently collaborate with teams, manage timelines, and maintain accurate records of their work.

Role of TechnosCOE in Embedded Learning

Most embedded system courses include real-world projects that allow students to apply their skills practically. TechnosCOE is one such project, an initiative designed to bridge the gap between theoretical knowledge and practical application. TechnosCOE offers students opportunities to work on cutting-edge projects involving IoT, robotics, and smart devices.

This initiative focuses on teamwork, innovation, and problem-solving, ensuring learners are industry-ready. Through the TechnosCOE, students are exposed to real-world challenges and learn how to apply embedded system principles to develop effective solutions.

Why Choose an Embedded System Development Course in India?

India is turning out to be a fast-growing hub for embedded technology. Industries like automotive, healthcare, and consumer electronics will have a vast number of opportunities. Embedded system development courses offered in India will ensure expert faculty members, state-of-the-art labs, and industrial collaborations. They also offer internship and placement support, which proves to be perfect for career growth.

Conclusion

The course on embedded system development course in India not only gives the students technical expertise but also prepares them for dynamic and rewarding careers. Mastering microcontrollers to developing IoT solutions, these skills are invaluable in today's technology-driven world. Initiatives like TechnosCOE further enhance the learning experience, making these courses a worthwhile investment for aspiring engineers.

[marketresearchauthority]

Emertxe Embedded Systems Online Course – A Gateway to a Thriving Career

Are you looking to kickstart your career in embedded systems but don't have the time to attend traditional classroom-based courses? Emertxe's Embedded Systems Online Course offers the perfect solution to gain in-depth knowledge and practical experience in this rapidly growing field from the comfort of your home.

Why Choose Emertxe’s Embedded Systems Online Course?

Emertxe is a leading provider of embedded systems training, offering specialized online courses designed to bridge the gap between academic knowledge and industry requirements. With its embedded systems online program, you can gain expertise in key areas such as microcontrollers, real-time operating systems (RTOS), device drivers, communication protocols, and much more.

Here’s why Emertxe’s embedded systems online course stands out:

1. Industry-Recognized Curriculum

Emertxe’s course content is developed in collaboration with industry experts and aligned with the latest trends and technologies in embedded systems. The online embedded systems program includes everything from the basics to advanced topics, ensuring that you are well-prepared for industry challenges.

2. Hands-on Learning Experience

Emertxe’s online embedded systems course focuses heavily on practical learning. You will work on real-time projects, assignments, and simulations that help solidify your understanding and improve your problem-solving skills. Emertxe’s online platform makes it easy to access tutorials, lab sessions, and code examples anytime, anywhere.

3. Experienced Trainers

Learn from highly qualified instructors who have hands-on experience in embedded systems development. Emertxe’s trainers are industry veterans who share their insights and guide you through the complexities of embedded system design and implementation.

4. Flexible Learning Pace

One of the key advantages of the Emertxe embedded systems online course is the flexibility it offers. You can learn at your own pace, revisit lessons whenever needed, and balance your studies with personal and professional commitments.

5. Job Placement Assistance

Emertxe provides placement assistance to its students. With its strong industry connections and a network of partner companies, Emertxe helps students get placed in top tech companies. Graduates of the online embedded systems program are highly sought after for roles such as Embedded Engineer, Firmware Developer, and Hardware Design Engineer.

Key Topics Covered in Emertxe’s Embedded Systems Online Course

Introduction to Embedded Systems: Learn the fundamentals of embedded systems, including their applications in various industries like automotive, consumer electronics, healthcare, and more.

Microcontroller Programming: Get hands-on experience in programming microcontrollers like ARM and AVR to build embedded solutions.

Real-Time Operating Systems (RTOS): Dive into RTOS concepts such as task scheduling, inter-process communication, and memory management to design responsive embedded systems.

Embedded C and C++ Programming: Master the core languages used in embedded systems programming and develop efficient, resource-constrained applications.

Device Drivers and Communication Protocols: Learn to develop device drivers and implement protocols like UART, SPI, I2C, and CAN to ensure seamless communication between components in embedded systems.

Embedded Linux: Explore the power of Linux in embedded systems and understand how to work with Linux kernel, drivers, and file systems.

Career Opportunities After Completing Emertxe’s Embedded Systems Online Course

Graduating from Emertxe’s embedded systems online program opens the doors to a wide range of career opportunities. The demand for skilled embedded systems professionals is soaring in sectors like automotive, aerospace, telecommunications, and consumer electronics. Emertxe’s curriculum equips you with the expertise needed to take on roles such as:

Embedded Systems Engineer

Firmware Developer

Embedded Software Developer

Hardware Engineer

Embedded Systems Consultant

How to Enroll in Emertxe’s Embedded Systems Online Course

Enrolling in the Emertxe embedded systems online course is simple. Visit the Emertxe website, select the online course option, and follow the easy steps to complete your registration. With flexible payment plans and a dedicated support team, Emertxe ensures that the entire process is smooth and hassle-free.

Final Thoughts

Emertxe's embedded systems online course is the perfect way to build a solid foundation in embedded systems while balancing your existing commitments. With a comprehensive curriculum, hands-on projects, and job placement assistance, Emertxe ensures that you are ready to take on exciting career opportunities in embedded systems development.

Ready to kickstart your career in embedded systems? Visit Emertxe Embedded Systems Online Course and enroll today!

[sdizdar]

Mixtile Edge 2 Kit– AI based bee detection and tracking

Here I describe usage of Mixtile Edge 2 Kit in agriculture, bee detection, which can be essential for health and survival of bees.

Story

Mixtile is professional IoT hardware solution provider specialized in Linux and Android-based embedded systems.Mixtile Edge 2 Kit is high-performance ARM single board computer. It comes in variants of 2GB of LPDDR4 DRAM and 16GB eMMC Flash storage, or 4GB of LPDDR4 DRAM and 32GB eMMC Flash storage. This single board computer comes with preinstalled Android 11, and it runs Ubuntu Linux operating system in Android container. It comes with large connectivity options (Bluetooth, 4G/5G Cellular, GPS, and Lora, Zigbee and Z-Wave). For those, you will need module, but it comes with default onboard Wi-Fi connectivity, Gigabit Ethernet Port (RJ45) and Serial Port (RS-485). Because it comes with RS-485 port, which is industrial standard, and it comes within a strong metal case, it seems to me that it can be really used in industrial projects. I used official Raspberry Pi 5 power supply in order to power up my Mixtile Edge 2 Kit.So, an idea came to me why not to use it in agriculture, bee detection, which can be essential for health and survival of bees.This project will cover setting up Mixtile Edge 2 Kit, and custom photo dataset form video in order to train custom YOLOv5 bee detection model. YOLOv5 models must be trained on labelled data in order to learn classes of objects in that data.I gathered data from video and trained model on my PC.To train a model, I used python and typed in command line:

python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights best.pt

My training results are summarized in the following table:

Training results

From this table you can see that images are divided into 4 detection classes:

Bee

Drone

Pollenbee

Queen

Example for each class is summarized in a table below:

Bee classes

1. Getting started

First, I will write about software part of the project, and later on steps of starting the recognition.

1.1 What is YOLOv5?

If you have been in the field of machine learning and deep learning for some time now, there is a high chance that you have already heard about YOLO. YOLO is short for You Only Look Once. It is a family of single-stage deep learning-based object detectors. It was written using Python language, and the framework used is PyTorch.

To ease control, I connected usb mouse to the one of three Mixtile Edge 2 Kit USB3 port. I used Ubuntu Linux for this project. Ubuntu on container is installed in Android system of Mixtile Edge 2 Kit by default. When you boot Mixtile Edge 2 Kit, you get Android OS. Since I wanted to access Edge 2 Kit remotely, and get easier control, I installed droidVNC server from this link:

It is an Android VNC server using Android 5+ APIs. It does not require root access.

I started the VNC server, connected with VNC Viewer and I got the following Android 11 screen:

Android 11

After that, I installed SimpleSSHD from this link:

SimpleSSHD is a SSH server Android app, based on Dropbear.It allows user access (user ssh) or full root access (by setting the login shell to /system/xbin/su) (if root is allowed).

After I installed SSH server, I connected to it via putty SSH terminal. Username and Password are root/root.

Com.hubware.ubuntu is ubuntu on a container and we are connected to it immidiately.

Now we are going to install required software.

First, you will need to upgrade Ubuntu by typing in the command: apt-get upgrade.

Second, I installed python by typing: apt-get install python.

You will also need pip, the package installer for Python.

2. Installing the YOLOv5 Environment

To start off we first clone the YOLOv5 repository and install dependencies. This will set up our programming environment to be ready to running object detection training and inference commands.

Install git: apt-get install git

Clone YOLOv5 repository:

git clone https://github.com/ultralytics/yolov5

Move to YOLOv5 folder:

cd yolov5

Install dependencies:

pip install -r requirements.txt

Wait some time to download and install all requirement packages, I waited 25 minutes, because there are a lot of python packages to install besides YOLOv5. YOLOv5 needs numpy package, scipy, OpenCV, etc.

The putty connection and installation process looks like below:

I transferred my model best.pt to the yolov5 installation folder via SCP, with MobaXterm.

You can simply download my model immidiate by typing:

wget https://github.com/sdizdarevic/beedetectionyolov5/raw/main/best.pt

Also, download original video by typing:

wget https://sdizdarevic.typepad.com/cr/bees-orig.mp4

Now, the final step is detection, and we are interested in the “result” content video.

python3 detect.py --weights best.pt --source bees-orig.mp4

The process of detection looks like below:

In the last lines from last picture we can see the detected number of bees at any point in time.

The summarized short steps to follow are below:

git clone https://github.com/ultralytics/yolov5

cd yolov5

pip install -r requirements.txt

wget https://github.com/sdizdarevic/beedetectionyolov5/raw/main/best.pt

wget https://sdizdarevic.typepad.com/cr/bees-orig.mp4

python3 detect.py --weights best.pt --source

Demonstrated videos are on urls with detection finished completely on Mixtile Edge 2 Kit. Output video is in folder runs/detect/exp2.

Original video:

Result video:

Last, but not less important: If you want to safely turn off your Mixtile Edge 2 Kit, I recommend you to install Shutdown (no Root) application: https://play.google.com/store/apps/details?id=com.samiadom.Shutdown&hl=en.

3.Conclusion:

After testing I found out that the Mixtile Edge 2 Kit is designed with wide range of applications, from industrial applications, IOT devices, smart home automation, to more than capable AI and edge detection. It is low powered device, with a lot of built-in connectivity options.

I would like to thank amazing Mixtile people for creating this amazing peace of hardware and especially for sending me the Mixtile Edge 2 Kit. Also, Mixtile nurtures the open source values and software, and I believe more people and companies will be involved in making projects with this board.

All in all, I recommend this board for implementing types of projects I described here.

[this-week-in-rust]

This Week in Rust 528

Hello and welcome to another issue of This Week in Rust! Rust is a programming language empowering everyone to build reliable and efficient software. This is a weekly summary of its progress and community. Want something mentioned? Tag us at @ThisWeekInRust on Twitter or @ThisWeekinRust on mastodon.social, or send us a pull request. Want to get involved? We love contributions.

This Week in Rust is openly developed on GitHub and archives can be viewed at this-week-in-rust.org. If you find any errors in this week's issue, please submit a PR.

Updates from Rust Community

Official

Announcing Rust 1.75.0

Project/Tooling Updates

Rustdoc JSON in 2023

2023 in Review: Establishing Rust as a Godot 4 language

Rust9x update: Rust 1.76.0-beta

Announcing smol-macros, smol-hyper and smol-axum

Rust Language Bootstrap Team Progress Report 2023

gitoxide: The year in retrospective, and what's to come

Observations/Thoughts

A few fast solutions for Advent of Code 2023

An Update on Writing Memory Safety Bugs

Testing Your Embedded Rust

avatar.png

Arc vs String, is Arc really faster?

Iggy.rs - building message streaming in Rust

Getting Started with Loco in Rust: Part 1

Printing errors in Rust

[video] Rust 1.75.0: 54 highlights in 20 minutes!

Miscellaneous

Test command line application written in Rust

Open Source Applications written in Rust

Prompt - read input from Standard Input (STDIN) in Rust

Testing Your Embedded Rust (feat. embedded-hal-mock and explosions)

[video] Rust Release Train 1.75

[video] Rust 1.75.0: 54 highlights in 20 minutes

Crate of the Week

This week's crate is fast_pool, a fast async pool based on the flume channel crate.

Thanks to zhuxiujia for the self-suggestion!

Please submit your suggestions and votes for next week!

Call for Participation; projects and speakers

CFP - Projects

Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

* Hyperswitch - [FEATURE]: Implement Code cov for local system using makefile * Hyperswitch - [FEATURE]: Setup code coverage for local tests & CI * Hyperswitch - [FEATURE]: Add domain type for client secret * Hyperswitch - [FEATURE]: Have get_required_value to use ValidationError in OptionExt

If you are a Rust project owner and are looking for contributors, please submit tasks here.

CFP - Speakers

Are you a new or experienced speaker looking for a place to share something cool? This section highlights events that are being planned and are accepting submissions to join their event as a speaker.

If you are an event organizer hoping to expand the reach of your event, please submit a link to the submission website either through a PR to TWiR or on the [Rust-lang forums].[link TBD]

Updates from the Rust Project

194 pull requests were merged in the last week

rustc_lint: Enforce rustc::potential_query_instability lint

rustc_lint: Prevent triplication of various lints

unused_bindings: also walk bindings created by if-let guards

change rustc_codegen_ssa's atomic_cmpxchg interface to return a pair of values

coverage: avoid a possible query stability hazard in CoverageCounters

coverage: prepare mappings separately from injecting statements

coverage: unexpand spans with find_ancestor_inside_same_ctxt

don't drop a hir node after lowering

don't suggest writing a bodyless arm if the pattern can never be a never pattern

don't validate / lint MIR before each pass

enable profiler in dist-powerpc-linux

fix infinite loop in <BoundConstness as Display>

fix invalid check-cfg Cargo feature diagnostic help

fix parenthesization of subexprs containing statement boundary

fix: correct the args for disambiguate the associated function diagnostic

fix: diagnostic for casting reference to slice

introduce const Trait (always-const trait bounds)

simplify Parser::ident_or_error

simplify bootstrap --check-cfg arguments

solaris support on bootstrap lock

subtree sync for rustc_codegen_cranelift

suggest => → >= in comparisons

utilize the unused llvm-tools option

miri: fix integer overflow ICEs from round_up_to_next_multiple_of

miri: NaN non-determinism for intrinsics and libm functions

miri: support for tempfile crate on UNIX hosts

implement constant propagation on top of MIR SSA analysis

only store StableCrateId once in DefPathTable

shrink span encoding further

openbsd: available_parallelism: use the right API

cargo: cargo add - fix for adding features from repository with multiple packages

cargo: cargo fix: always inherit the jobserver

cargo: fix fix::fix_in_dependency to not rely on rustc

cargo: rustfix: support inserting new lines

rustdoc-search: count path edits with separate edit limit

rustdoc: treat query string + as space

clippy: check for redundant matches! with Ready, Pending, V4, V6

clippy: [doc_markdown]: Add "WebGL2", "WebGPU" to default doc_valid_idents

clippy: add external macro checks to iter_without_into_iter and into_iter_without_iter

clippy: don't lint default_numeric_fallback on return and local assigned macro calls with type stated

clippy: extend unconditional_recursion to check for ToString implementations

clippy: add manual_is_variant_and lint

clippy: add new lint pub_underscore_fields

clippy: suggest str.lines when splitting at hard-coded newlines

clippy: make mutex_atomic more type aware

clippy: new lint: empty_enum_variants_with_brackets

clippy: new lint: thread_local_initializer_can_be_made_const

clippy: new lint: eager_transmute

clippy: remove mitigations for incorrect node args

rust-analyzer: fix SyntaxContextID using incorrect self IDs

rust-analyzer: fix out-of-bounds panic in some macros due to unhandled self_ref

Rust Compiler Performance Triage

Overall, this week had very few regressions and a moderate amount of improvements. The two biggest improvements came in how metadata was being encoded including a change to only store StableCrateId once in DefPathTable which yielded a 0.3% average improvement across 79 different benchmarks.

Triage done by @rylev. Revision range: 1ab783112..67b6975

Summary:

(instructions:u) mean range count Regressions ❌ (primary) 0.7% [0.3%, 1.5%] 8 Regressions ❌ (secondary) 0.8% [0.2%, 1.3%] 23 Improvements ✅ (primary) -0.6% [-2.6%, -0.2%] 121 Improvements ✅ (secondary) -5.2% [-62.5%, -0.2%] 53 All ❌✅ (primary) -0.5% [-2.6%, 1.5%] 129

2 Regressions, 3 Improvements, 1 Mixed; 0 of them in rollups 46 artifact comparisons made in total

Full report here

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

No RFCs were approved this week.

Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

RFCs

No RFCs entered Final Comment Period this week.

Tracking Issues & PRs

[disposition: merge] rustdoc: search for tuples and unit by type with ()

Language Reference

No Language Reference RFCs entered Final Comment Period this week.

Unsafe Code Guidelines

No Unsafe Code Guideline RFCs entered Final Comment Period this week.

New and Updated RFCs

RFC: New range types for Edition 2024

Add RFC to discuss RustConf 2024 Steering Committee

Call for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization. The following RFCs would benefit from user testing before moving forward:

No RFCs issued a call for testing this week.

If you are a feature implementer and would like your RFC to appear on the above list, add the new call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

Upcoming Events

Rusty Events between 2024-01-03 - 2024-01-31 🦀

Virtual

2024-01-03 | Virtual (Indianapolis, IN, US) | Indy Rust

Indy.rs - with Social Distancing

2024-01-06 | Virtual (Kampala, UG) | Rust Circle

Rust Circle Meetup

2024-01-09 | Virtual (Dallas, TX, US) | Dallas Rust

Second Tuesday

2024-01-11 | Virtual (Charlottesville, NC, US) | Charlottesville Rust Meetup

Crafting Interpreters in Rust Collaboratively

2024-01-11 | Virtual (Nürnberg, DE) | Rust Nuremberg

Rust Nürnberg online

2024-01-16 | Virtual (Washington, DC, US) | Rust DC

Mid-month Rustful

2024-01-17 | Virtual (Vancouver, BC, CA) | Vancouver Rust

Rust Study/Hack/Hang-out

2024-01-21 | Virtual | Rust Maven

Web development with Rocket - In English

2024-01-23 | Virtual (Halifax, NS, CA) | Rust Halifax

Rust&Tell - Halifax

2024-01-24 | Virtual (Berlin, DE) | WeAreDevelopers Community

WeAreDevelopers LIVE - Rust Day

2024-01-25 | Virtual (Charlottesville, NC, US) | Charlottesville Rust Meetup

Crafting Interpreters in Rust Collaboratively

2024-01-28 | Virtual (Wrocław, PL) | Stacja IT Wrocław

Wprowadzenie do języka Rust

2024-01-30 | Virtual (Buffalo, NY, US) | Buffalo Rust User Group

Buffalo Rust User Group

2024-01-30 | Virtual (Dallas, TX, US) | Dallas Rust

Last Tuesday

Europe

2024-01-10 | Cologne, DE | Rust Cologne

Game development in Rust

2024-01-11 | Reading, UK | Reading Rust Workshop

Reading Rust Meetup at Browns

2024-01-11 | Wrocław, PL | Rust Wrocław

Rust Meetup #36

2024-01-13 | Tampere, FI | Finland Rust-lang Group

January Meetup

2024-01-16 | Leipzig, DE | Rust - Modern Systems Programming in Leipzig

Async in Rust

2024-01-17 | Praha / Prague, CZ | Rust Prague

Rust Meetup Renewed

2024-01-17 | Zurich, CH | Rust Zurich

How to Community - January Meetup

2024-01-23 | Aarhus, DK | Rust Aarhus

Hack and Learn

North America

2024-01-06 | Boston, MA, US | Boston Rust Meetup

Beacon Hill Rust Lunch

2024-01-08 | Chicago, IL, US | Deep Dish Rust

Rust Hack Night

2024-01-09 | Seattle, WA, US | Cap Hill Rust Coding/Hacking/Learning

Rusty Coding/Hacking/Learning Night

2024-01-09 | Minneapolis, MN, US | Minneapolis Rust Meetup

Minneapolis Rust Meetup Happy Hour

2024-01-09 | New York, NY, US | Rust NYC

Rust NYC Monthly Meetup: A Deep Dive into Tower by Adrien Guillo

2024-01-14 | Cambridge, MA, US | Boston Rust Meetup

Alewife Rust Lunch

2024-01-16 | San Francisco, CA, US | San Francisco Rust Study Group

Rust Hacking in Person

2024-01-17 | Chicago, IL, US | Deep Dish Rust

Rust Happy Hour

2024-01-18 | Seattle, WA, US | Seattle Rust User Group

Seattle Rust User Group Meetup

2024-01-22 | Boston, MA, US | Boston Rust Meetup

North End Rust Lunch

2024-01-24 | Austin, TX, US | Rust ATX

Rust Lunch - Fareground

2024-01-30 | Cambridge, MA, US | Boston Rust Meetup

Harvard Square Rust Lunch

If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

Jobs

Please see the latest Who's Hiring thread on r/rust

Quote of the Week

Some people don't believe in life after death... Rust doesn't believe in magic after compilation.

– Stephan Sokolow on rust-users

Thanks to Todd Fleming for the suggestion!

Please submit quotes and vote for next week!

This Week in Rust is edited by: nellshamrell, llogiq, cdmistman, ericseppanen, extrawurst, andrewpollack, U007D, kolharsam, joelmarcey, mariannegoldin, bennyvasquez.

Email list hosting is sponsored by The Rust Foundation

Discuss on r/rust

[lanshengic]

IAR fully supports Infineon's latest TRAVEO T2G CYT6BJ body control MCU family products

According to the information received by Lansheng Technology, it has fully supported the latest CYT6BJ series in Infineon's TRAVEO™ T2G body control MCU family. IAR Embedded Workbench for Arm is a complete embedded development solution, equipped with highly optimized compiler and build tools, code analysis tools C-STAT and C-RUN, and powerful debugging functions. This enables developers working on complex automotive body electronics applications to take full advantage of the features of the TRAVEO™ T2G MCU to create innovative designs with high code quality. IAR Embedded Workbench for Arm supports AUTOSAR and provides a functional safety version to help customers accelerate product certification.

Clara Volkmar, Director Product Marketing Body and Driver Information, Infineon's Automotive Division, said: "Infineon's TRAVEO™ T2G microcontrollers provide a compact solution to meet the needs of modern automotive body electronics systems and can take advantage of IAR Professional and suitable third-party development tools such as Embedded Workbench for Arm to achieve efficient development. Thanks to such a powerful tool partner as IAR, developers can quickly realize their demanding automotive applications even with our latest CYT6BJ series chips project."

By supporting all available TRAVEO™ T2G series chips, including the latest CYT6BJ series, IAR Embedded Workbench for Arm ensures compatibility and provides developers with highly optimized build tools and advanced debugging features: such as complex code and data interrupts Points, runtime stack analysis, call stack visualization, code coverage analysis, and integrated monitoring of power consumption contribute to a seamless development experience. With the code analysis tools C-STAT and C-RUN, developers can fully control the code quality.

For companies with functional safety requirements, IAR Embedded Workbench for Arm offers a functional safety version certified by TÜV SÜD (certified according to ISO 26262 requirements). This functional safety version is also certified according to IEC 61508, IEC 62304, EN 50128, EN 50657, IEC 60730, ISO 13849, IEC 62061, IEC 61511 and ISO 25119. For companies using continuous integration (CI) workflows and automated build and test processes, IAR Build Tools is also available for Linux-based frameworks. In addition, IAR's professional technical support, training and flexible licenses enable all customers to find a solution that suits their specific needs.

Lansheng Technology Limited, which is a spot stock distributor of many well-known brands, we have price advantage of the first-hand spot channel, and have technical supports. 

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[wolfliving]

TensorFlow Lite for microcontrollers

*Maybe I had better get used to this.

https://www.tensorflow.org/lite/microcontrollers

TensorFlow Lite for Microcontrollers is an experimental port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with only kilobytes of memory.

It doesn't require operating system support, any standard C or C++ libraries, or dynamic memory allocation. The core runtime fits in 16 KB on an Arm Cortex M3, and with enough operators to run a speech keyword detection model, takes up a total of 22 KB.

There are example applications demonstrating the use of microcontrollers for tasks including wake word detection, gesture classification from accelerometer data, and image classification using camera data.

To try the example applications and learn how to use the API, read Get started with microcontrollers.

TensorFlow Lite for Microcontrollers is written in C++ 11 and requires a 32-bit platform. It has been tested extensively with many processors based on the Arm Cortex-M Series architecture, and has been ported to other architectures including ESP32.

The framework is available as an Arduino library. It can also generate projects for development environments such as Mbed. It is open source and can be included in any C++ 11 project.

There are example applications available for the following development boards:

Arduino Nano 33 BLE Sense

SparkFun Edge

STM32F746 Discovery kit

Adafruit EdgeBadge

Adafruit TensorFlow Lite for Microcontrollers Kit

To learn more about the libraries and examples, see Get started with microcontrollers.

Microcontrollers are typically small, low-powered computing devices that are often embedded within hardware that requires basic computation, including household appliances and Internet of Things devices. Billions of microcontrollers are manufactured each year.

Microcontrollers are often optimized for low energy consumption and small size, at the cost of reduced processing power, memory, and storage. Some microcontrollers have features designed to optimize performance on machine learning tasks.

By running machine learning inference on microcontrollers, developers can add AI to a vast range of hardware devices without relying on network connectivity, which is often subject to bandwidth and power constraints and results in high latency. Running inference on-device can also help preserve privacy, since no data has to leave the device.

To deploy a TensorFlow model to a microcontroller, you will need to follow this process:

Create or obtain a TensorFlow model

The model must be small enough to fit on your target device after conversion, and it can only use supported operations. If you want to use operations that are not currently supported, you can provide your own implementations.

Convert the model to a TensorFlow Lite FlatBuffer

You will convert your model into the standard TensorFlow Lite format using the TensorFlow Lite converter. You may wish to output a quantized model, since these are smaller in size and more efficient to execute.

Convert the FlatBuffer to a C byte array

Models are kept in read-only program memory and provided in the form of a simple C file. Standard tools can be used to convert the FlatBuffer into a C array.

Integrate the TensorFlow Lite for Microcontrollers C++ library

Write your microcontroller code to collect data, perform inference using the C++ library, and make use of the results.

Deploy to your device

Build and deploy the program to your device.

TensorFlow Lite for Microcontrollers is designed for the specific constraints of microcontroller development. If you are working on more powerful devices (for example, an embedded Linux device like the Raspberry Pi), the standard TensorFlow Lite framework might be easier to integrate.

The following limitations should be considered:

Support for a limited subset of TensorFlow operations

Support for a limited set of devices

Low-level C++ API requiring manual memory management

Training is not supported

Read Get started with microcontrollers to try the example applications and learn how to use the API....

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[planetarduino]

Fruit identification using Arduino and TensorFlow

By Dominic Pajak and Sandeep Mistry

Arduino is on a mission to make machine learning easy enough for anyone to use. The other week we announced the availability of TensorFlow Lite Micro in the Arduino Library Manager. With this, some cool ready-made ML examples such as speech recognition, simple machine vision and even an end-to-end gesture recognition training tutorial. For a comprehensive background we recommend you take a look at that article. 

In this article we are going to walk through an even simpler end-to-end tutorial using the TensorFlow Lite Micro library and the Arduino Nano 33 BLE 33 Sense’s colorimeter and proximity sensor to classify objects. To do this, we will be running a small neural network on the board itself. 

Arduino BLE 33 Nano Sense running TensorFlow Lite Micro

The philosophy of TinyML is doing more on the device with less resources – in smaller form-factors, less energy and lower cost silicon. Running inferencing on the same board as the sensors has benefits in terms of privacy and battery life and means its can be done independent of a network connection. 

The fact that we have the proximity sensor on the board means we get an instant depth reading of an object in front of the board – instead of using a camera and having to determine if an object is of interest through machine vision. 

In this tutorial when the object is close enough we sample the color – the onboard RGB sensor can be viewed as a 1 pixel color camera. While this method has limitations it provides us a quick way of classifying objects only using a small amount of resources. Note that you could indeed run a complete CNN-based vision model on-device. As this particular Arduino board includes an onboard colorimeter, we thought it’d be fun and instructive to demonstrate in this way to start with.

We’ll show a simple but complete end-to-end TinyML application can be achieved quickly and without a deep background in ML or embedded. What we cover here is data capture, training, and classifier deployment. This is intended to be a demo, but there is scope to improve and build on this should you decide to connect an external camera down the road. We want you to get an idea of what is possible and a starting point with tools available.

What you’ll need

Arduino Nano 33 BLE Sense

A micro USB cable

A desktop/laptop machine with a web browser 

Some objects of different colors 

About the Arduino board

The Arduino Nano 33 BLE Sense board we’re using here has an Arm Cortex-M4 microcontroller running mbedOS and a ton of onboard sensors – digital microphone, accelerometer, gyroscope, temperature, humidity, pressure, light, color and proximity. 

While tiny by cloud or mobile standards the microcontroller is powerful enough to run TensorFlow Lite Micro models and classify sensor data from the onboard sensors.

Setting up the Arduino Create Web Editor

In this tutorial we’ll be using the Arduino Create Web Editor – a cloud-based tool for programming Arduino boards. To use it you have to sign up for a free account, and install a plugin to allow the browser to communicate with your Arduino board over USB cable.

You can get set up quickly by following the getting started instructions which will guide you through the following:

Download and install the plugin

Sign in or sign up for a free account

(NOTE: If you prefer, you can also use the Arduino IDE desktop application. The setup for which is described in the previous tutorial.)

Capturing training data

We now we will capture data to use to train our model in TensorFlow. First, choose a few different colored objects. We’ll use fruit, but you can use whatever you prefer. 

Setting up the Arduino for data capture

Next we’ll use Arduino Create to program the Arduino board with an application object_color_capture.ino that samples color data from objects you place near it. The board sends the color data as a CSV log to your desktop machine over the USB cable.

To load the object_color_capture.ino application onto your Arduino board:

Connect your board to your laptop or PC with a USB cable

The Arduino board takes a male micro USB

Open object_color_capture.ino in Arduino Create by clicking this link

Your browser will open the Arduino Create web application (see GIF above).

Press OPEN IN WEB EDITOR

For existing users this button will be labeled ADD TO MY SKETCHBOOK

Press Upload & Save

This will take a minute

You will see the yellow light on the board flash as it is programmed

Open the serial Monitor

This opens the Monitor panel on the left-hand side of the web application

You will now see color data in CSV format here when objects are near the top of the board

Capturing data in CSV files for each object

For each object we want to classify we will capture some color data. By doing a quick capture with only one example per class we will not train a generalized model, but we can still get a quick proof of concept working with the objects you have to hand! 

Say, for example, we are sampling an apple:

Reset the board using the small white button on top.

Keep your finger away from the sensor, unless you want to sample it!

The Monitor in Arduino Create will say ‘Serial Port Unavailable’ for a minute

You should then see Red,Green,Blue appear at the top of the serial monitor

Put the front of the board to the apple. 

The board will only sample when it detects an object is close to the sensor and is sufficiently illuminated (turn the lights on or be near a window)

Move the board around the surface of the object to capture color variations

You will see the RGB color values appear in the serial monitor as comma separated data. 

Capture at a few seconds of samples from the object

Copy and paste this log data from the Monitor to a text editor

Tip: untick AUTOSCROLL check box at the bottom to stop the text moving

Save your file as apple.csv

Reset the board using the small white button on top.

Do this a few more times, capturing other objects (e.g. banana.csv, orange.csv). 

NOTE: The first line of each of the .csv files should read:

Red,Green,Blue

If you don’t see it at the top, you can just copy and paste in the line above. 

Training the model

We will now use colab to train an ML model using the data you just captured in the previous section.

First open the FruitToEmoji Jupyter Notebook in colab

Follow the instructions in the colab

You will be uploading your *.csv files 

Parsing and preparing the data

Training a model using Keras

Outputting TensorFlowLite Micro model

Downloading this to run the classifier on the Arduino 

With that done you will have downloaded model.h to run on your Arduino board to classify objects!

The colab will guide you to drop your .csv files into the file window, the result shown above

Normalized color samples captured by the Arduino board are graphed in colab

Program TensorFlow Lite Micro model to the Arduino board

Finally, we will take the model we trained in the previous stage and compile and upload to our Arduino board using Arduino Create. 

Open Classify_Object_Color.ino 

Your browser will open the Arduino Create web application:

Press the OPEN IN WEB EDITOR button

Import the  model.h you downloaded from colab using Import File to Sketch: 

Import the model.h you downloaded from colab

The model.h tab should now look like this

Compile and upload the application to your Arduino board 

This will take a minute

When it’s done you’ll see this message in the Monitor:

Put your Arduino’s RGB sensor near the objects you trained it with

You will see the classification output in the Monitor:

Classifier output in the Arduino Create Monitor

You can also edit the object_color_classifier.ino sketch to output emojis instead (we’ve left the unicode in the comments in code!), which you will be able to view in Mac OS X or Linux terminal by closing the web browser tab with Arduino Create in, resetting your board, and typing cat /cu/usb.modem[n]. 

Output from Arduino serial to Linux terminal using ANSI highlighting and unicode emojis

Learning more

The resources around TinyML are still emerging but there’s a great opportunity to get a head start and meet experts coming up 2-3 December 2019 in Mountain View, California at the Arm IoT Dev Summit. This includes workshops from Sandeep Mistry, Arduino technical lead for on-device ML and from Google’s Pete Warden and Daniel Situnayake who literally wrote the book on TinyML. You’ll be able to hang out with these experts and more at the TinyML community sessions there too. We hope to see you there!

Conclusion

We’ve seen a quick end-to-end demo of machine learning running on Arduino. The same framework can be used to sample different sensors and train more complex models. For our object by color classification we could do more, by sampling more examples in more conditions to help the model generalize. In future work, we may also explore how to run an on-device CNN. In the meantime, we hope this will be a fun and exciting project for you. Have fun!

Fruit identification using Arduino and TensorFlow was originally published on PlanetArduino

[emblogicsblog]

USB Device Driver Course Online

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Embedded Linux Kernel Internals using ARM and Device Drivers

"Embedded Linux Kernel Internals using ARM and Device Drivers" (ELKIADD) is an Ineffable, Comprehensive, Hands-on, project based career oriented, training program for BE, B Tech, ME, M Tech from the streams of Electronics, Computer Science, Information Technologies, Instrumentation or Master of Computer Applications.

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The concepts absorbed during this training should help you in any domain where electro-mechanical, electronic devices are designed. You will be efficient with respect to incorporating intelligencs in the devices.

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Emertxe Embedded Systems Course with Placement in Bangalore

In today’s technology-driven world, embedded systems are the backbone of innovations ranging from IoT devices to automotive technologies. If you're looking for a career in this high-demand sector, Emertxe’s Embedded Systems Course in Bangalore is your perfect launchpad. With industry-recognized training and a strong placement program, Emertxe is helping aspiring engineers transition into successful embedded systems professionals.

Why Choose Emertxe for Embedded Systems Training?

As one of India’s top embedded systems training institutes, Emertxe stands out for its comprehensive curriculum, experienced faculty, and robust placement support. Here’s why Emertxe is a preferred choice:

1. Industry-Relevant Curriculum

Emertxe’s embedded systems course is meticulously designed to align with current industry trends. The curriculum covers:

Embedded C and Data Structures

Linux Systems Programming

ARM-based Embedded Systems Design

RTOS (Real-Time Operating System)

IoT Systems and Embedded Firmware

This hands-on learning approach ensures that you’re industry-ready by the time you complete the course.

2. 100% Placement Guarantee

One of the biggest benefits of enrolling in Emertxe is the institute’s strong placement support. Emertxe has a stellar track record of placing students in top companies. Through rigorous training and mock interviews, the placement team ensures you’re well-prepared for job opportunities. Some of the hiring partners include:

Intel

Bosch

Samsung

Qualcomm

With a 100% placement guarantee, Emertxe has helped numerous students achieve their dream jobs.

3. Experienced Faculty

The training at Emertxe is led by industry veterans with years of experience in embedded systems and IoT. The faculty provides not only theoretical knowledge but also valuable industry insights and project-based learning.

4. Real-World Projects

The best way to master embedded systems is through practical experience. Emertxe’s course includes hands-on projects like:

Designing Embedded IoT Devices

Building ARM Cortex-Based Applications

Real-Time Systems Development

These projects help you gain real-world skills that can be directly applied in your future career.

5. Bangalore – The Hub for Tech Jobs

Bangalore, often referred to as the Silicon Valley of India, offers immense opportunities for embedded systems professionals. Being located in Bangalore, Emertxe provides students with exposure to a thriving tech ecosystem, making it easier to connect with top companies during placement drives.

Course Highlights at Emertxe

Duration: 6 months

Mode of Learning: Classroom and online

Eligibility: Engineering graduates (ECE, EEE, CSE, and IT)

Certification: Upon course completion, you will receive an industry-recognized certification in embedded systems.

Success Stories of Emertxe Alumni

Many students have transformed their careers through Emertxe. From working in core embedded systems jobs to landing high-paying roles in MNCs, the opportunities are endless. Here are a few success stories from Emertxe alumni:

Sanjeev Kumar – Placed at Qualcomm with a package of 7 LPA

Meghana Reddy – Working as an Embedded Developer at Bosch

Rohan Sharma – Landed an IoT engineer role at Intel

Join Emertxe Today and Kickstart Your Embedded Systems Career!

If you're passionate about technology and want a rewarding career in embedded systems, Emertxe’s Embedded Systems Course with Placement in Bangalore is the ideal starting point. With cutting-edge training and unmatched placement support, you’ll be well-equipped to enter this lucrative field.

Enroll now and become part of a leading tech institute that transforms your career.

[vlsi-guru]

Best Embedded Systems Training Institute in Bangalore - VLSIGuru

Embedded Systems Training - VLSIGuru Institute is highly rated among the Best Embedded Training Institutes in Bangalore. Embedded Systems Training in Bangalore is targeted towards BE, ME Fresher’s & Engineers working in other domains

Embedded Systems Training (VG-EMBEDDED) is  16 weeks course  to enable engineers develop skills in full breadth of Embedded systems starting from Advanced C,  Data Structures, LPC1313 Micro  controller architecture with ARM Cortex m3 processor, Linux internals and C++. Training is offered in both classroom and online training modes. Embedded Systems Training in Bangalore is targeted towards BE, ME fresher’s and Engineers working in other domains planning to make career in Embedded systems domain. Course structure is framed keeping in mind the current industry requirements.

Embedded Systems Training divided in to multiple aspects, with training focused on all aspects from Embedded Systems basics,   all   industry standard micro controller architectures, Embedded C programming, standard interface protocols and   multiple   industry   standard projects on Automotive. All these topics are covered with practical examples based on these boards.

Embedded Systems Training course also covers multiple   hands-on projects based on industry standard Automotive LPC1313 micro controller. Learning starts from developing simple board based projects to complex projects involving   multiple peripherals connected to make a complex embedded system. All these projects are done from scratch. Course curriculum also ensures that student does these projects hands-on with trainer guidance as part of dedicated lab sessions.

Embedded Systems Training also includes 30+ detailed assignments (10 in C and Data structures, 10 in Micro controllers and Peripherals, 10 in Linux Internals). These assignments are prepared by industry experts covering all aspects from embedded systems. Student gets to work on these assignments with complete guidance from trainers and student learning is evaluated using completion of assignments as the sole criteria.

VLSIGuru Institute has expertise in both VLSI and Embedded system domain. We have trained more than 1000 graduates over last 5 years since training was started in 2012. VLSIGuru Institute is rated among the Best Embedded Training Institutes in Bangalore.

https://www.vlsiguru.com/

https://www.vlsiguru.com/embedded-systems-training/

9986194191

contact@vlsiguru.com

[vlsitraininginbangalore]

Best Embedded Systems Training Institute in Bangalore - VLSIGuru

Embedded Systems Training - VLSIGuru Institute is highly rated among the Best Embedded Training Institutes in Bangalore. Embedded Systems Training in Bangalore is targeted towards BE, ME Fresher’s & Engineers working in other domains

Embedded Systems Training (VG-EMBEDDED) is  16 weeks course  to enable engineers develop skills in full breadth of Embedded systems starting from Advanced C,  Data Structures, LPC1313 Micro  controller architecture with ARM Cortex m3 processor, Linux internals and C++. Training is offered in both classroom and online training modes. Embedded Systems Training in Bangalore is targeted towards BE, ME fresher’s and Engineers working in other domains planning to make career in Embedded systems domain. Course structure is framed keeping in mind the current industry requirements.

Embedded Systems Training divided in to multiple aspects, with training focused on all aspects from Embedded Systems basics,   all   industry standard micro controller architectures, Embedded C programming, standard interface protocols and   multiple   industry   standard projects on Automotive. All these topics are covered with practical examples based on these boards.

Embedded Systems Training course also covers multiple   hands-on projects based on industry standard Automotive LPC1313 micro controller. Learning starts from developing simple board based projects to complex projects involving   multiple peripherals connected to make a complex embedded system. All these projects are done from scratch. Course curriculum also ensures that student does these projects hands-on with trainer guidance as part of dedicated lab sessions.

Embedded Systems Training also includes 30+ detailed assignments (10 in C and Data structures, 10 in Micro controllers and Peripherals, 10 in Linux Internals). These assignments are prepared by industry experts covering all aspects from embedded systems. Student gets to work on these assignments with complete guidance from trainers and student learning is evaluated using completion of assignments as the sole criteria.

VLSIGuru Institute has expertise in both VLSI and Embedded system domain. We have trained more than 1000 graduates over last 5 years since training was started in 2012. VLSIGuru Institute is rated among the Best Embedded Training Institutes in Bangalore.

https://www.vlsiguru.com/

https://www.vlsiguru.com/embedded-systems-training/

9986194191

contact@vlsiguru.com

[embeddedsystemscourseonline-blog]

Best Embedded Systems Training Institute in Bangalore - VLSIGuru

Embedded Systems Training - VLSIGuru Institute is highly rated among the Best Embedded Training Institutes in Bangalore. Embedded Systems Training in Bangalore is targeted towards BE, ME Fresher’s & Engineers working in other domains

Embedded Systems Training (VG-EMBEDDED) is  16 weeks course  to enable engineers develop skills in full breadth of Embedded systems starting from Advanced C,  Data Structures, LPC1313 Micro  controller architecture with ARM Cortex m3 processor, Linux internals and C++. Training is offered in both classroom and online training modes. Embedded Systems Training in Bangalore is targeted towards BE, ME fresher’s and Engineers working in other domains planning to make career in Embedded systems domain. Course structure is framed keeping in mind the current industry requirements.

Embedded Systems Training divided in to multiple aspects, with training focused on all aspects from Embedded Systems basics,   all   industry standard micro controller architectures, Embedded C programming, standard interface protocols and   multiple   industry   standard projects on Automotive. All these topics are covered with practical examples based on these boards.

Embedded Systems Training course also covers multiple   hands-on projects based on industry standard Automotive LPC1313 micro controller. Learning starts from developing simple board based projects to complex projects involving   multiple peripherals connected to make a complex embedded system. All these projects are done from scratch. Course curriculum also ensures that student does these projects hands-on with trainer guidance as part of dedicated lab sessions.

Embedded Systems Training also includes 30+ detailed assignments (10 in C and Data structures, 10 in Micro controllers and Peripherals, 10 in Linux Internals). These assignments are prepared by industry experts covering all aspects from embedded systems. Student gets to work on these assignments with complete guidance from trainers and student learning is evaluated using completion of assignments as the sole criteria.

VLSIGuru Institute has expertise in both VLSI and Embedded system domain. We have trained more than 1000 graduates over last 5 years since training was started in 2012. VLSIGuru Institute is rated among the Best Embedded Training Institutes in Bangalore.

https://www.vlsiguru.com/

https://www.vlsiguru.com/embedded-systems-training/

9986194191

contact@vlsiguru.com

[wolfliving]

The Jetson Nano

*Let’s see if anybody needs this Maker AI gizmo.

*It’s a press release.

https://nvidianews.nvidia.com/news/nvidia-announces-jetson-nano-99-tiny-yet-mighty-nvidia-cuda-x-ai-computer-that-runs-all-ai-models?ncid=so-twi-gj-78738&linkId=100000005468164

GPU Technology Conference—NVIDIA today announced the Jetson Nano™, an AI computer that makes it possible to create millions of intelligent systems.

The small but powerful CUDA-X™ AI computer delivers 472 GFLOPS of compute performance for running modern AI workloads and is highly power-efficient, consuming as little as 5 watts.

Unveiled at the GPU Technology Conference by NVIDIA founder and CEO Jensen Huang, Jetson Nano comes in two versions — the $99 devkit for developers, makers and enthusiasts and the $129 production-ready module for companies looking to create mass-market edge systems.

Jetson Nano supports high-resolution sensors, can process many sensors in parallel and can run multiple modern neural networks on each sensor stream. It also supports many popular AI frameworks, making it easy for developers to integrate their preferred models and frameworks into the product.

Jetson Nano joins the Jetson™ family lineup, which also includes the powerful Jetson AGX Xavier™ for fully autonomous machines and Jetson TX2 for AI at the edge. Ideal for enterprises, startups and researchers, the Jetson platform now extends its reach with Jetson Nano to 30 million makers, developers, inventors and students globally.

“Jetson Nano makes AI more accessible to everyone — and is supported by the same underlying architecture and software that powers our nation’s supercomputers,” said Deepu Talla, vice president and general manager of Autonomous Machines at NVIDIA. “Bringing AI to the maker movement opens up a whole new world of innovation, inspiring people to create the next big thing.”

Jetson Nano Developer Kit The power of AI is largely out of reach for the maker community and in education because typical technologies do not pack enough computing power and lack an AI software platform.

At $99, the Jetson Nano Developer Kit brings the power of modern AI to a low-cost platform, enabling a new wave of innovation from makers, inventors, developers and students. They can build AI projects that weren’t previously possible and take existing projects to the next level — mobile robots and drones, digital assistants, automated appliances and more.

The kit comes with out-of-the-box support for full desktop Linux, compatibility with many popular peripherals and accessories, and ready-to-use projects and tutorials that help makers get started with AI fast. NVIDIA also manages the Jetson developer forum, where people can get answers to technical questions.

“The Jetson Nano Developer Kit is exciting because it brings advanced AI to the DIY movement in a really easy-to-use way,” said Chris Anderson of DIY Robocars, DIY Drones and the Linux Foundation’s Dronecode project. “We’re planning to introduce this technology to our maker communities because it’s a powerful, fun and affordable platform that’s a great way to teach deep learning and robotics to a broader audience.”

Jetson Nano Module In the past, companies have been constrained by the challenges of size, power, cost and AI compute density. The Jetson Nano module brings to life a new world of embedded applications, including network video recorders, home robots and intelligent gateways with full analytics capabilities. It is designed to reduce overall development time and bring products to market faster by reducing the time spent in hardware design, test and verification of a complex, robust, power-efficient AI system.

The design comes complete with power management, clocking, memory and fully accessible input/outputs. Because the AI workloads are entirely software defined, companies can update performance and capabilities even after the system has been deployed.

“Cisco Collaboration is on a mission to connect everyone, everywhere for rich and immersive meetings,” said Sandeep Mehra, vice president and general manager for Webex Devices at Cisco. “Our work with NVIDIA and use of the Jetson family lineup is key to our success. We’re able to drive new experiences that enable people to work better, thanks to the Jetson platform’s advanced AI at the edge capabilities.”

To help customers easily move AI and machine learning workloads to the edge, NVIDIA worked with Amazon Web Services to qualify AWS Internet of Things Greengrass to run optimally with Jetson-powered devices such as Jetson Nano.

“Our customers span very diverse industries, including energy management, industrial, logistics, and smart buildings and homes,” said Dirk Didascalou, vice president of IoT, Amazon Web Services, Inc. “Players in all of these industries are building intelligence and computer vision into their applications to take action at the edge in near real time. AWS IoT Greengrass allows our customers to perform local inference on Jetson-powered devices and send pertinent data back to the cloud to improve model training.”

One Software Stack Across the Entire Jetson Family NVIDIA CUDA-X is a collection of over 40 acceleration libraries that enable modern computing applications to benefit from NVIDIA’s GPU-accelerated computing platform. JetPack SDK™ is built on CUDA-X and is a complete AI software stack with accelerated libraries for deep learning, computer vision, computer graphics and multimedia processing that supports the entire Jetson family.

The JetPack includes the latest versions of CUDA, cuDNN, TensorRT™ and a full desktop Linux OS. Jetson is compatible with the NVIDIA AI platform, a decade-long, multibillion-dollar investment that NVIDIA has made to advance the science of AI computing.

Reference Platforms to Prototype Quickly NVIDIA has also created a reference platform to jumpstart the building of AI applications by minimizing the time spent on initial hardware assembly. NVIDIA JetBot™ is a small mobile robot that can be built with off-the-shelf components and open sourced on GitHub.

Jetson Nano System Specs and Software Key features of Jetson Nano include:

GPU: 128-core NVIDIA Maxwell™ architecture-based GPU

CPU: Quad-core ARM® A57

Video: 4K @ 30 fps (H.264/H.265) / 4K @ 60 fps (H.264/H.265) encode and decode

Camera: MIPI CSI-2 DPHY lanes, 12x (Module) and 1x (Developer Kit)

Memory: 4 GB 64-bit LPDDR4; 25.6 gigabytes/second

Connectivity: Gigabit Ethernet

OS Support: Linux for Tegra®

Module Size: 70mm x 45mm

Developer Kit Size: 100mm x 80mm

Availability The NVIDIA Jetson Nano Developer Kit is available now for $99. The Jetson Nano module is $129 (in quantities of 1,000 or more) and will begin shipping in June. Both will be sold through NVIDIA’s main global distributors. Developer kits can also be purchased from maker channels, Seeed Studio and SparkFun.

About NVIDIA NVIDIA‘s (NASDAQ: NVDA) invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. More information at http://nvidianews.nvidia.com/.

Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, performance and abilities of Jetson Nano, Jetson Nano Developer Kit, Jetson Nano module and JetPack SDK; Jetson Nano running all AI models and ability to create millions of intelligent systems; Jetson Nano supporting many frameworks making it easy for developers to integrate their models and frameworks into the product; Jetson products extending its reach to users globally; Jetson Nano making AI more accessible to everyone and bringing AI to the maker movement opening up a new world of innovation and inspiring the next big thing; Jetson Nano Developer Kit bringing the power of modern AI, enabling a new wave of innovation; enabling AI projects that were not possible before and taking existing projects to the next level; excitement over the Jetson Nano Developer Kit bringing AI to the DIY movement and the reasons for the planned introduction of the technology to maker communities; the Jetson Nano module opening up a new world of embedded applications, its ability to bring products to market faster and companies’ ability to update performance and capabilities after the system has been deployed; Cisco’s mission, work with NVIDIA and Jetson being the key to its success and its ability to drive new experiences that enable people to work better due to Jetson; the benefits of Jetson and AWS Internet of Things Greengrass working together and it enabling customers to perform inference on Jetson devices and help improve model training; players in industries building intelligence and computer visions into their applications; and the availability of the Jetson Nano Developer Kit and Jetson Nano module are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2019 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, CUDA, Jetson, Jetson AGX Xavier, Jetson Nano, NVIDIA JetBot, NVIDIA JetPack and TensorRT are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.