#TensorFlow Lite for Microcontrollers

[bermondseysblog]

Tensorflow Lite for Microcontrollers | Bermondsey Electronics Limited

Explore the future of embedded AI with Bermondsey Electronics Limited. Harness the power of TensorFlow Lite for Microcontrollers to bring intelligence to your devices. Elevate your products with advanced machine learning capabilities.

[tia003]

What are power optimization techniques in embedded AI systems?

Power efficiency is a critical concern in embedded AI systems, particularly for battery-operated and resource-constrained devices. Optimizing power consumption ensures longer operational life, reduced heat dissipation, and improved overall efficiency. Several key techniques help achieve this optimization:

Dynamic Voltage and Frequency Scaling (DVFS): This technique adjusts the processor’s voltage and clock speed dynamically based on workload requirements. Lowering the frequency during idle or low-computation periods significantly reduces power consumption.

Efficient Hardware Design: Using low-power microcontrollers (MCUs), dedicated AI accelerators, and energy-efficient memory architectures minimizes power usage. AI-specific hardware, such as Edge TPUs and NPUs, improves performance while reducing energy demands.

Sleep and Low-Power Modes: Many embedded AI systems incorporate deep sleep, idle, or standby modes when not actively processing data. These modes significantly cut down power usage by shutting off unused components.

Model Quantization and Pruning: Reducing the precision of AI models (quantization) and eliminating unnecessary model parameters (pruning) lowers computational overhead, enabling energy-efficient AI inference on embedded systems.

Energy-Efficient Communication Protocols: For IoT-based embedded AI, using low-power wireless protocols like Bluetooth Low Energy (BLE), Zigbee, or LoRa helps reduce power consumption during data transmission.

Optimized Code and Algorithms: Writing power-efficient code, using optimized AI frameworks (e.g., TensorFlow Lite, TinyML), and reducing redundant computations lower energy demands in embedded AI applications.

Adaptive Sampling and Edge Processing: Instead of continuously transmitting all sensor data to the cloud, embedded AI systems perform on-device processing, reducing communication power consumption.

Mastering these power optimization techniques is crucial for engineers working on intelligent devices. Enrolling in an embedded system certification course can help professionals gain expertise in designing efficient, low-power AI-driven embedded solutions.

[siliconsignalsblog]

Latest Trends and Innovations in Embedded Systems

 Embedded systems continue to revolutionize the way we interact with technology. From consumer electronics to industrial applications, the latest advancements in embedded systems are driving efficiency, connectivity, and performance like never before. In this blog, we delve into the latest trends and news shaping the embedded systems landscape in 2025.

1. The Rise of AI at the Edge

One of the most significant trends in embedded systems is the integration of artificial intelligence (AI) at the edge. Edge AI allows devices to process data locally, reducing latency and improving efficiency. This technology is particularly impactful in applications such as autonomous vehicles, smart manufacturing, and healthcare.

Recent developments include:

Enhanced processing power in microcontrollers (MCUs) and system-on-modules (SoMs) to support AI workloads.

Tools like TensorFlow Lite and PyTorch for optimized AI model deployment on edge devices.

Use cases such as predictive maintenance, real-time object detection, and voice recognition.

2. Matter Standard in IoT

The Matter standard is shaping the future of IoT by enabling seamless interoperability between smart devices. Embedded engineers are leveraging this standard to create smarter, more user-friendly products.

Key highlights:

Major players like Apple, Google, and Amazon adopting the Matter standard.

Increased focus on security and scalability for smart home and industrial IoT (IIoT).

Development of Matter-compliant devices to ensure compatibility across platforms.

3. Open-Source Hardware Gaining Momentum

Open-source hardware is enabling engineers and hobbyists to accelerate development cycles while reducing costs. Platforms like Raspberry Pi, Arduino, and BeagleBone remain popular, but new entrants are offering specialized solutions for complex embedded applications.

Noteworthy updates include:

Growth in community-driven projects for niche applications.

Availability of development kits with pre-configured software and hardware.

Increased adoption in education and prototyping.

4. Low-Power Design for Sustainability

Sustainability is becoming a cornerstone of embedded system design. Low-power solutions are critical for battery-operated devices, wearables, and IoT sensors, ensuring prolonged operational life and reduced energy consumption.

Recent breakthroughs:

Advanced low-power MCUs like the ARM Cortex-M series.

Innovative energy harvesting techniques for self-sustaining devices.

Design strategies focusing on dynamic voltage scaling and efficient power management.

5. Advances in Real-Time Operating Systems (RTOS)

Real-Time Operating Systems (RTOS) are crucial for time-sensitive applications, such as robotics, aerospace, and medical devices. Recent updates in RTOS focus on improved security, scalability, and ease of integration.

Notable advancements:

Enhanced support for multi-core processors.

Lightweight RTOS options for constrained devices.

Growing popularity of platforms like FreeRTOS and Zephyr.

6. Embedded Security Takes Center Stage

With the proliferation of connected devices, embedded security is more critical than ever. The latest security measures are aimed at protecting data and ensuring device integrity.

Recent updates:

Adoption of hardware security modules (HSMs) for cryptographic operations.

Implementation of secure boot and trusted execution environments (TEEs).

Compliance with standards like IEC 62443 for industrial security.

Conclusion

The embedded systems industry is evolving rapidly, with innovations driving smarter, more efficient, and secure solutions. Staying updated on these trends is vital for engineers, developers, and businesses aiming to remain competitive in this dynamic field. From Edge AI to sustainable designs, the future of embedded systems holds immense potential to transform industries and improve everyday life.

Stay ahead with our insights on embedded technology trends. Contact us today to explore how we can help bring your embedded vision to life!

[codezup]

TensorFlow Lite on Microcontrollers: A Complete Step-by-Step Guide

Introduction TensorFlow Lite on Microcontrollers is a subset of the TensorFlow Lite micro framework, optimized for microcontrollers. It allows you to deploy machine learning models on resource-constrained devices, making it an essential tool for IoT development. In this tutorial, we’ll walk you through the process of implementing TensorFlow Lite on Microcontrollers, covering the technical…

[omshinde5145]

Embedded AI Market | Future Growth Aspect Analysis to 2030

The Embedded AI Market was valued at USD 8.9 billion in 2023 and will surpass USD 21.5 billion by 2030; growing at a CAGR of 13.5 % during 2024 - 2030.Embedded AI refers to the integration of artificial intelligence algorithms and processing capabilities directly into hardware devices. Unlike traditional AI, which often requires connection to powerful cloud computing systems, embedded AI operates locally on edge devices such as sensors, microcontrollers, or other hardware components. This enables real-time decision-making and data analysis with reduced latency and power consumption.

This convergence of AI and embedded systems is unlocking new possibilities for smarter, autonomous, and responsive devices that can analyze and act upon data instantly without needing to send it to remote servers for processing.

Market Growth and Key Drivers

The global embedded AI market is expanding rapidly, driven by several key factors:

Advancements in Edge Computing

The proliferation of edge computing has played a pivotal role in the growth of embedded AI. Edge devices with built-in AI capabilities are able to process data locally, reducing the need for constant communication with cloud servers. This is particularly crucial for applications requiring immediate decision-making, such as autonomous vehicles, drones, and industrial automation.

Increased Demand for IoT Devices

The Internet of Things (IoT) is a major contributor to the growth of embedded AI. IoT devices are embedded in everyday objects like smart home appliances, wearable devices, and industrial equipment, gathering data in real time. By integrating AI, these devices can offer predictive maintenance, enhanced user experiences, and optimized operational efficiency.

Read More about Sample Report: https://intentmarketresearch.com/request-sample/embedded-ai-market-3623.html

Enhanced AI Algorithms

AI algorithms have become more efficient and powerful, enabling them to operate in low-power, resource-constrained environments like embedded systems. With advancements in AI frameworks, such as TensorFlow Lite and PyTorch Mobile, the ability to deploy AI models on edge devices is now more accessible than ever.

Industry 4.0 and Smart Manufacturing

Industry 4.0 emphasizes automation, smart factories, and connected machinery. Embedded AI plays a critical role in optimizing processes in manufacturing, such as predictive maintenance, quality control, and energy management. Machines equipped with AI can autonomously monitor their own performance, identify inefficiencies, and make adjustments in real time.

Rise of Autonomous Systems

The push toward autonomous systems, especially in the automotive industry, is driving embedded AI adoption. Self-driving cars, drones, and robots rely on embedded AI to process vast amounts of sensor data, make real-time decisions, and navigate complex environments without human intervention.

Key Sectors Driving Embedded AI Adoption

Automotive Industry

The automotive industry is at the forefront of embedded AI adoption. AI-driven features like autonomous driving, advanced driver-assistance systems (ADAS), and predictive maintenance are all powered by embedded AI systems. These technologies enable cars to analyze real-time road conditions, detect potential hazards, and make instant decisions, enhancing safety and efficiency.

Healthcare

In healthcare, embedded AI is transforming medical devices and diagnostic tools. AI-powered wearables can monitor patients' vital signs in real time, providing healthcare professionals with actionable insights for early diagnosis and personalized treatment plans. Moreover, embedded AI systems in medical imaging devices can assist in detecting diseases like cancer with higher accuracy.

Consumer Electronics

From smart speakers to home security systems, embedded AI is driving innovation in the consumer electronics space. Devices are becoming more intuitive, offering personalized experiences through voice recognition, gesture control, and facial recognition technologies. These AI-driven enhancements have revolutionized how consumers interact with their devices.

Industrial Automation

Embedded AI in industrial automation is enabling smarter, more efficient factories. AI-powered sensors and controllers can optimize production processes, predict equipment failures, and reduce downtime. As industries move toward fully autonomous operations, embedded AI will play an integral role in managing complex industrial systems.

Challenges in the Embedded AI Market

Despite its rapid growth, the embedded AI market faces several challenges. Developing AI algorithms that can operate efficiently in resource-constrained environments is complex. Power consumption, heat generation, and the limited processing capabilities of embedded devices must all be carefully managed. Moreover, there are concerns around data privacy and security, particularly in industries handling sensitive information, such as healthcare and finance.

Another challenge is the lack of standardization across embedded AI platforms, which can hinder widespread adoption. To address this, industry stakeholders are collaborating on developing open standards and frameworks to streamline AI deployment in embedded systems.

Ask for Customization Report: https://intentmarketresearch.com/ask-for-customization/embedded-ai-market-3623.html

The Future of Embedded AI

The future of embedded AI looks promising, with continued advancements in hardware, AI algorithms, and edge computing technologies. As AI capabilities become more efficient and affordable, their integration into everyday devices will become increasingly ubiquitous. In the coming years, we can expect to see even greater adoption of embedded AI in smart cities, autonomous transportation systems, and advanced robotics.

Moreover, the convergence of 5G technology with embedded AI will further accelerate innovation. With faster, more reliable connectivity, edge devices equipped with AI will be able to process and transmit data more efficiently, unlocking new use cases across various industries.

Conclusion

The embedded AI market is revolutionizing industries by enabling devices to think, analyze, and act autonomously. As the demand for smarter, more responsive technology grows, embedded AI will continue to transform sectors such as automotive, healthcare, industrial automation, and consumer electronics. With its ability to provide real-time insights and decision-making at the edge, embedded AI is set to play a central role in the next wave of technological innovation.

[lanshengic]

Microchip launches MPLAB® Machine Learning Development Kit to help developers easily integrate machine learning into MCUs and MPUs

【Lansheng Technology News】Microchip Technology Inc. recently launched a new MPLAB® machine learning development toolkit, providing a complete integrated workflow to simplify machine learning model development. Available across Microchip's broad portfolio of microcontrollers (MCUs) and microprocessors (MPUs), this software toolkit enables developers to quickly and efficiently add machine learning inference.

Rodger Richey, vice president of Microchip's Development Systems Business Unit, said: "Machine learning is the new normal for embedded controllers. Leveraging machine learning at the edge can make products more efficient, more secure, and less power-intensive than systems that rely on cloud communications for processing. Lower. Designed specifically for embedded engineers, Microchip’s unique integrated solutions are the first to support not only 32-bit MCUs and MPUs, but also 8-bit and 16-bit devices, enabling efficient product development.”

Machine learning works by using a set of algorithms to analyze and generate patterns from large data sets to support decision-making. Machine learning is generally faster, easier to update, and more accurate than human processing. Microchip customers can leverage this new set of tools to enable predictive maintenance solutions to accurately predict potential problems with equipment used in a variety of industrial, manufacturing, consumer and automotive applications.

MPLAB Machine Learning Development Kit helps engineers build efficient, small-footprint machine learning models. Powered by AutoML, the toolkit eliminates many repetitive, tedious, and time-consuming model building tasks, including extraction, training, validation, and testing. It also provides model optimization capabilities to meet the memory constraints of MCUs and MPUs.

When used in conjunction with the MPLAB X integrated development environment (IDE), the new toolkit provides a complete solution. It can be easily implemented by people with almost no knowledge of machine learning programming, saving the cost of hiring data scientists. It also has advanced features that meet the needs of experienced machine learning designers.

Microchip also offers the option to extract models from TensorFlow Lite and use them in any MPLAB Harmony v3 project. MPLAB Harmony v3 is a fully integrated embedded software development framework that provides flexible, interoperable software modules to simplify the development of value-added functions and shorten product time to market. Additionally, the VectorBlox™ Accelerator Software Development Kit (SDK) provides the most energy-efficient artificial intelligence/machine learning (AI/ML) inference capabilities based on convolutional neural networks (CNN) using PolarFire® FPGAs.

The MPLAB Machine Learning Development Kit provides the necessary tools to design and optimize edge products that run machine learning inference. Visit the Microchip Machine Learning Solutions page to learn more about streamlining your development process, reducing costs and accelerating time to market with Microchip’s intuitive machine learning tools.

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. 

Our main brands: STMicroelectronics, Toshiba, Microchip, Vishay, Marvell, ON Semiconductor, AOS, DIODES, Murata, Samsung, Hyundai/Hynix, Xilinx, Micron, Infinone, Texas Instruments, ADI, Maxim Integrated, NXP, etc

To learn more about our products, services, and capabilities, please visit our website at http://www.lanshengic.com

[planetarduino]

This tinyML device counts your squats while you focus on your form

Getting in your daily exercise is vital to living a healthy life and having proper form when squatting can go a long way towards achieving that goal without causing joint pain from doing them incorrectly. The Squats Counter is a device worn around the thigh that utilizes machine learning and TensorFlow Lite to automatically track the user’s form and count how many squats have been performed. 

Creator Manas Pange started his project by flashing the tf4micro-moition-kit code to a Nano 33 BLE Sense, which features an onboard three-axis accelerometer. From there, he opened the Tiny Motion Trainer Experiment by Google that connects to the Arduino over Bluetooth and captures many successive samples of motion. After gathering enough proper and improper form samples, Manas trained, tested, and deployed the resulting model to the board.

Every time a proper squad is performed, the counter ticks down by one until it reaches a predefined goal.

For more details about the Squats Counter, which was recently named a winner in the TensorFlow Lite for Microcontroller Challenge, you can view its GitHub repository here. 

The post This tinyML device counts your squats while you focus on your form appeared first on Arduino Blog.

This tinyML device counts your squats while you focus on your form was originally published on PlanetArduino

[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....

[cogtropolis]

Benchmarking TensorFlow Lite for microcontrollers on Linux SBCs

In this post, I’ll show you the results of benchmarking the TensorFlow Lite for microcontrollers (tflite-micro) API not on various MCUs this time, but on various Linux SBCs (Single-Board Computers). For this purpose I’ve used a code which I’ve originally written to test and compare the tflite-micro API which is written in C++ and the tflite python API. Link: Benchmarking TensorFlow Lite for microcontrollers on Linux SBCs via www.stupid-projects.com Read the full article

[usacounselingcredit]

Houston Texas Appliance Parts: Ambiq Launches AI SDK for Ultra-Low Power MCUs

Houston Texas Appliance Parts

Ambiq Launches AI SDK for Ultra-Low Power MCUs

by Houston Texas Appliance Parts on Friday 17 February 2023 06:21 PM UTC-05

Ambiq Micro is the latest microcontroller maker to build its own AI-focused software development kit (SDK). The combination of Ambiq's Neural Spot AI SDK with its ultra-low power sub-threshold and near-threshold technologies will enable efficient inference: Ambiq's figures have keyword spotting at less than a milliJoule (mJ). This efficiency will suit IoT devices, especially wearables, which are already a big market for the company.

Artificial intelligence applications on Cortex-M devices require specialized software stacks over and above what's available with open-source frameworks, such as TensorFlow Lite for Microcontrollers, since there are so many challenges involved in fine-tuning performance, Carlos Morales, Ambiq Micro's VP of AI, told EE Times.

"[Arm's CMSIS-NN] has optimized kernels that use [Arm's cores] really well, but getting the data in and moving it to the next layer means there are a lot of transformations that happen, and [Arm] has to be general about that," he said. "If you carefully design your datapath, you don't have to do those transformations, you can just rip out the middle of those things and just call them one by one–and that gets very efficient."

Neural Spot's libraries are based on an optimized version of CMSIS-NN, with added features for fast Fourier transforms (FFTs), among others. Morales points out that, unlike cloud AI, embedded AI is focused in large part on about a dozen classes of models, so it's an easier subset to optimize for.

"A voice-activity detector running in TensorFlow would be terrible, you'd just be spending all your time loading tensors back and forth. But you write it [at a lower level], and suddenly you're doing it in two or three milliseconds, which is great," he said.

Neural Spot includes a model zoo. (Source: Ambiq Micro)

Further headaches include mismatches between Python and the C/C++ code that runs on embedded devices.

"We created a set of tools that let you treat your embedded device as if it were part of Python," Morales said. "We use remote procedure calls from inside your Python model to execute it on the eval board."

Remote procedure calls enable easy comparison of, for example, Python's feature extractor or Mel spectrogram calculator to what's running on the eval board (a Mel spectrogram is a representation of audio data used in audio processing).

Neural Spot includes an open-source model zoo with health (ECG classifier) and speech detection/processing examples. Speech processing includes models for voice activity detection, keyword detection and speech to intent. Ambiq is working on AI models for speech enhancement (background noise cancellation) and computer vision models, including person detection and object classification.

The Neural Spot AI SDK is built on Ambiq Suite—Ambiq's libraries for controlling power and memory configurations, communicating with sensors and managing SoC peripherals. Neural Spot simplifies these configuration options using presets for AI developers who may not be familiar with sub-threshold hardware.

Ambiq's Neural Spot SDK targets specialised AI developers, domain experts and system integrators. (Source: Ambiq Micro)

The new SDK is designed for all fourth-generation Apollo chips, but the Apollo4 Plus SoC is particularly well suited for always-on AI applications, Morales said. It features an Arm Cortex-M4 core with 2 MB embedded MRAM, and 2.75 MB SRAM. There's also a graphics accelerator, two MIPI lanes, and some family members have Bluetooth Low Energy radios.

Current consumption for the Apollo4 Plus is as low as 4 μA/MHz when executing from MRAM, and there are advanced deep sleep modes. With such low power consumption, he said, "suddenly you can do a lot more things," when running AI in resource-constrained environment.

"There are a lot of compromises you have to make, for example, reducing precision, or making shallower models because of latency or power requirements…all that stuff you're stripping out because you want to stay in the power budget, you can put back in," Morales added.

He also pointed out that while AI acceleration is important to saving power, other parts of the data pipeline are just as important, including sensing data, analog-to-digital conversion and moving data around memory: Collecting audio data, for example, might take several seconds while inference is complete in tens of milliseconds. Data collection might thus account for the majority of the power usage.

Ambiq compared internal power measurements for the Apollo4 Plus running benchmarks from MLPerf Tiny, with published results for other microcontrollers. Ambiq's figures for the Apollo4 Plus have the energy consumption (µJ/inference) at roughly 8 to 13× lower, compared with another Cortex-M4 device. The keyword-spotting inference benchmark used less than a milliJoule, and person detection used less than 2 mJ.

Ambiq's internal energy results for its Cortex-M4-equipped Apollo 4 Plus series, versus competing microcontrollers (competing results taken from MLPerf Tiny). (Source: Ambiq Micro)

Sub-threshold operation

Ambiq achieves such low power operation using sub-threshold and near-threshold operation. While big power savings are possible using sub-threshold voltages, it is not straightforward, Scott Hanson, founder and CTO of Ambiq Micro, told EE Times in an earlier interview.

"At its surface, sub-threshold and near-threshold operation are quite simple: You're just dialing down the voltage. Seemingly, anybody could do that, but it turns out that it's, in fact, quite difficult," he said. "When you turn down voltage into the near-threshold or sub-threshold range, you end up with huge sensitivities to temperature, to process, to voltage, and so it becomes very difficult to deploy conventional design techniques."

Ambiq's secret sauce is in how the company mitigates for these variables.

"When faced with temperature and process variations, it's critical to center a supply voltage at a value that can compensate for those temperature and process fluctuations, so we have a unique way of regulating voltage across process and temperature that that allows subthreshold and near-threshold operations to be reliable and robust," Hanson said.

Ambiq's technology platform, Spot, uses "50 or 100" design techniques to deal with this, with techniques spanning analog, digital and memory design. Most of these techniques are at the circuit level; many classic building block circuits, including examples like the bandgap reference circuit, don't work when running in subthreshold mode and require re-engineering by Ambiq. Other challenges include how to distribute the clock and how to assign voltage domains.

Running at lower voltage does come with a tradeoff: Designs have to run slower. That's why, Hanson said, Ambiq started by applying its sub-threshold ideas in the embedded space. Twenty-four or 48 MHz was initially sufficient for ultra-low power wearables, where Ambiq holds about half the market share today. However, customers quickly increased their clock speed requirements. Ambiq achieved this by introducing more dynamic voltage and frequency scaling (DVFS) operating points—customers run 99% of the time in sub-threshold or near-threshold mode, but when they need a boost in compute, they can increase the voltage to run at higher frequency.

"Over time, you'll see more DVFS operating points from Ambiq because we want to support really low voltages, medium voltages and high voltages," Hanson said.

Other items on the technology roadmap for Ambiq include more advanced process nodes, architectural enhancements that increase performance without raising voltage and dedicated MAC accelerators (for both AI inference and filter acceleration).

The post Ambiq Launches AI SDK for Ultra-Low Power MCUs appeared first on EE Times.

Pennsylvania Philadelphia PA Philadelphia February 17, 2023 at 03:00PM

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[iot-talk]

Google Opens Pre-Orders for the Coral Dev Board Micro, Its First Microcontroller Development Board - Hackster.io

[lewuathe]

Annoucements in TensorFlow Dev Summit 2019

TensorFlow Dev Summit is an annual conference held by Google collecting a bunch of topics around TensorFlow framework. I usually watch the conference via live stream on YouTube. But fortunately, I got a chance to attend the conference this time. There is a sear number of exciting announcements in the conference so I list up the interesting items in this article.

Table Of Contents

- TensorFlow 2.0 - TensorFlow Lite - TensorFlow.js 1.0 - Others

TensorFlow 2.0

TensorFlow 2.0 alpha has just been released today. The biggest change introduced by the version is the simplified API by Keras and default eager mode. It was a little hard to write the complicated control flow as a TensorFlow graph in the past. You need to be familiar with tf.where or tf.select in order to write the conditions. That makes programmers have a difficult time to write a code executing what they want to.

From 2.0, you can use tf.function to write a complex control flow as TensorFlow graph. tf.function is just an annotation to be attached to the Python function. TensorFlow compiler automatically resolves the dependency on the Tensor and create a graph.

@tf.function def f(x): while tf.reduce_sum(x) > 1: x = tf.tanh(x) return x f(tf.ranfom.uniform([10]))

In the above example, function f will be a TensorFlow graph because it depends on the given tensors, a and b even we don’t define the ops for while control flow. That makes the development far easier because we can write the control flow of TensorFlow graph as we write the Python code. It is achieved by overloading some method like __if__ or __while__ under the hood. It also the necessity of tf.control_dependencies which was needed to update multiple variables properly. It contributes to reducing the complexity of graph construction too.

So overall you do not need to write the following things anymore.

tf.session.run

tf.control_dependencies

tf.global_variables_initializer

tf.cond, tf.while_loop

Please the here to look into more detail about TensorFlow 2.0 and Autograph feature.

Improvements of TensorFlow Lite

One amazing thing I found in the conference was the TensorFlow project puts a significant amount of resource to the edge computing which is happening on the mobile device, wearable, browsers, and smart speakers. TensorFlow Lite is a symbolic product to accelerate the ML in Edge device. The ML in edge device is thought to be required by the following reasons.

Fast Interactive Application

Data Privacy

By running the ML application on the client side, we can eliminate the overhead of sending the data between server and client. It is beneficial especially in the environment where we have a limited amount of network resource or bandwidth. It also protects data privacy by avoiding to send data to the server. So the demand for the ML in edge device is growing more and more.

TensorFlow Lite is a project to create a lightweight TensorFlow model running in the edge device. By delegating the processing to Edge TPU, they achieve 62x faster inference time at maximum combining with quantization.

They have also a super tiny model that is only tens of kilobytes so that we can put it into the microcontrollers. Sparkfun is an edge demo board powered by TensorFlow. Exciting thing is that we could get the board as a gift of attending the conference.

This kind of gift always makes me fun because we can try to use what we’ve learned. So I’m going to try the tiny model running on the microcontroller later. In my opinion, the evolution of the ML in the edge device is the most interesting field. Please take look into the video for more detail around TensorFlow Lite.

TensorFlow.js 1.0

The reason why I’m involving with TensorFlow project is this. I keep contributing to TensorFlow.js since it has been published. (It was named deeplearn.js initially) It gave me an opportunity to write a book, “Deep Learning in the Browser”. Evolving the project I’m involved game me a great joy.

Now it has been released as 1.0 reaching a kind of milestone. In addition to core components, there are multiple supporting libraries around TensorFlow.js.

tfjs-layers

tfjs-data

tfjs-vis

Those libraries enable us to have a similar experience using TensorFlow core libraries. It must accelerate the application development in the client side supporting JavaScript runtime. Actually, TensorFlow.js project will support various kind of platform such as Electron and React Native so that we can use the same application code in many platforms.

Here is another interesting slide. The performance of TensorFlow.js in Chrome browser is becoming faster since its initial release. Therefore, TensorFlow.js can be said to be a production-ready platform that supports client-side ML application sufficiently.

I also had a chance to talk with TensorFlow.js team members about the enhancements and development plan happening in near future. At the same time, I’m looking forward to these kinds of things, I want to contribute these things to make TensorFlow.js more powerful. Here is the video about the announcement of TensorFlow.js 1.0.

Others

Last but not least, I’m going to introduce some other changes I found at the conference.

Released tf-agents to accelerate reinforcement learning by using TensorFlow

Tensorboard can be embedded in Google Colab and Jupyter notebook

The new conference TensorFlow World will be held Oct 28-31

Two new online courses are available to learn TensorFlow at Cousera and Udacity

The full videos of presentation introduced in the conference are available on YouTube. Please take a look if you want to know further.

Thanks!

source http://www.lewuathe.com/annoucements-in-tensorflow-dev-summit-2019.html

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[siliconsignalsblog]

Embedded Systems News and Insights: 

As we approach the close of another year, the embedded systems industry continues to make significant strides across multiple domains, from automotive to IoT, medical devices, and beyond. In this December update, we explore key developments, trends, and challenges that have shaped the industry recently and highlight what lies ahead.

1. The Rise of Real-Time Edge AI

Edge computing and artificial intelligence are merging more seamlessly than ever before. Companies are now leveraging real-time AI at the edge, enabling applications in autonomous vehicles, predictive maintenance, and smart manufacturing. Key players have introduced compact, power-efficient modules featuring AI accelerators capable of handling sophisticated machine learning workloads directly on embedded platforms.

Key Takeaway: Developers are prioritizing solutions like TensorFlow Lite and PyTorch Mobile for on-device inference to reduce latency and enhance data privacy.

2. The Surge in RISC-V Adoption

RISC-V architecture has seen tremendous adoption this year, with new microcontrollers and SoCs flooding the market. Its open-source nature continues to attract innovators who are developing tailored solutions for cost-sensitive and performance-critical applications.

Industry Buzz: Several announcements during recent trade shows showcased the growing ecosystem around RISC-V, including robust development tools, pre-built software stacks, and commercial support.

3. Embedded Systems in Medical Devices

The medical industry has embraced embedded systems to achieve groundbreaking results. From wearable health monitors to sophisticated diagnostic tools, embedded technology plays a critical role in enhancing healthcare delivery. Recent advancements include:

Integration of AI for early diagnosis.

Custom SoCs for real-time medical imaging.

Secure, HIPAA-compliant data transfer protocols.

Future Focus: Continued emphasis on cybersecurity will shape the roadmap for medical device manufacturers.

4. Automotive Embedded Systems: Software-Defined Vehicles

The concept of software-defined vehicles (SDVs) is transitioning from theory to reality. Manufacturers are heavily investing in embedded platforms that allow OTA updates, enabling features like advanced driver-assistance systems (ADAS), enhanced infotainment, and energy optimization for EVs.

Emerging Trend: The integration of QNX OS and Android Automotive OS is becoming the norm for delivering robust and user-friendly interfaces.

5. IoT Security: A Persistent Challenge

As IoT devices proliferate, so do the challenges around securing embedded systems. Cyberattacks targeting connected devices underscore the importance of implementing strong authentication methods, encrypted communication, and regular firmware updates.

Pro Tip: Developers are adopting trusted execution environments (TEEs) and hardware security modules (HSMs) to strengthen device security.

6. Standards in Focus: Matter 1.2 and Interoperability

The IoT ecosystem continues to rally around Matter, a unified standard designed to simplify interoperability between smart home devices. The recent release of Matter 1.2 introduces support for additional device types, including robotic vacuums and air quality monitors.

Developer Insight: Adopting Matter ensures future-proofing and cross-platform compatibility for new IoT products.

7. Embedded Linux and Real-Time Enhancements

Linux remains the backbone of many embedded systems, and real-time capabilities are gaining more prominence. The latest kernel updates bring improved preemption models, making Linux more viable for mission-critical applications in robotics, industrial automation, and telecommunications.

Highlight: Developers are increasingly leveraging Yocto Project for building customized Linux distributions tailored to their specific hardware needs.

8. Upcoming Trends for 2025

Looking ahead, the embedded systems industry will see:

Greater adoption of AI-powered design automation tools.

Expansion of edge computing in industries like agriculture and energy.

Accelerated development of low-power SoCs for wearable tech.

Final Thought: Collaboration between hardware and software teams will be more critical than ever to address the complexities of modern embedded systems.

If you're looking to collaborate or require expert solutions in embedded systems design and development, reach out to us! Let’s innovate and build the future together. Contact us today to discuss how we can bring your ideas to life.

[lanshengic]

Microchip launches MPLAB machine learning development toolkit to help developers easily integrate machine learning into MCUs and MPUs

【Lansheng Technology News】September 8, 2023 - Machine learning is becoming a standard requirement for embedded designers to develop or improve various products. To meet this need, Microchip Technology Inc. recently launched the new MPLAB® machine learning development toolkit, which provides a complete integrated workflow to simplify machine learning model development. Available across Microchip’s broad portfolio of microcontrollers and microprocessors, this software toolkit helps developers quickly and efficiently add machine learning inference.

Rodger Richey, vice president of Microchip's Development Systems Business Unit, said: "Machine learning is the new normal for embedded controllers. Leveraging machine learning at the edge can make products more efficient, safer, and more power-efficient than systems that rely on cloud communications for processing. Lower. Microchip’s unique integrated solution is designed for embedded engineers and is the first to support not only 32-bit MCUs and MPUs, but also 8-bit and 16-bit devices, enabling efficient product development.”

Machine learning works by using a set of algorithms to analyze and generate patterns from large data sets to support decision-making. Machine learning is generally faster, easier to update, and more accurate than human processing. Microchip customers can leverage this new set of tools to enable predictive maintenance solutions to accurately predict potential problems with equipment used in a variety of industrial, manufacturing, consumer and automotive applications.

MPLAB Machine Learning Development Kit helps engineers build efficient, small-footprint machine learning models. Powered by AutoML, the toolkit eliminates many repetitive, tedious and time-consuming model building tasks, including extraction, training, validation and testing. It also provides model optimization capabilities to meet the memory constraints of MCUs and MPUs.

When used in conjunction with the MPLAB It also has advanced features that meet the needs of experienced machine learning designers.

Microchip also offers the option to extract models from TensorFlow Lite and use them in any MPLAB Harmony v3 project. MPLAB Harmony v3 is a fully integrated embedded software development framework that provides flexible, interoperable software modules to simplify the development of value-added functions and shorten product time to market. Additionally, the VectorBlox™ Accelerator Software Development Kit (SDK) provides the most energy-efficient artificial intelligence/machine learning (AI/ML) inference capabilities based on convolutional neural networks (CNN) using PolarFire® FPGAs.

The MPLAB Machine Learning Development Kit provides the necessary tools to design and optimize edge products that run machine learning inference. Visit the Microchip Machine Learning Solutions page to learn more about simplifying your development process, reducing costs and accelerating time to market with Microchip’s intuitive machine learning tools.

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