#verilog generate

[inbabylontheywept]

Hey Mr. Babylon, I got a bs in electrical a while ago and after a really rough last semester felt super burnt out. I'm terrified to actually work now. I do love the field and met a lot of really great people in ECE but feel paralyzed. What should I do?

I don't think I have good advice for this. I know that the longer you've been without work since graduating, the harder it becomes to get that first job, so I'd encourage you to really jump on that, and if you're scared of the job because you think it will be as scary as college, the answer is probably not. I specialized in power and transmission lines in college though, so my work has been with the utility companies followed by the DoD, neither of which is known for being that stressful. If you majored in Verilog and want to work in a software environment, you're going to make boatloads but you're gonna be more tense. I dunno. But you're really not locked in by your undergrad classes, so you can pick jobs that seem more or less stressful.

I guess, apply like crazy, be curious about the jobs, don't be picky, but also, absolutely, positively, do not work for a company run by Musk, General Electric, or any Navy Shipyards. Those three are notoriously awful. Everything else is generally fun.

Good luck!

[electronictechub]

Understanding FPGA Architecture: Key Insights

Introduction to FPGA Architecture

Imagine having a circuit board that you could rewire and reconfigure as many times as you want. This adaptability is exactly what FPGAs offer. The world of electronics often seems complex and intimidating, but understanding FPGA architecture is simpler than you think. Let’s break it down step by step, making it easy for anyone to grasp the key concepts.

What Is an FPGA?

An FPGA, or Field Programmable Gate Array, is a type of integrated circuit that allows users to configure its hardware after manufacturing. Unlike traditional microcontrollers or processors that have fixed functionalities, FPGAs are highly flexible. You can think of them as a blank canvas for electrical circuits, ready to be customized according to your specific needs.

How FPGAs Are Different from CPUs and GPUs

You might wonder how FPGAs compare to CPUs or GPUs, which are more common in everyday devices like computers and gaming consoles. While CPUs are designed to handle general-purpose tasks and GPUs excel at parallel processing, FPGAs stand out because of their configurability. They don’t run pre-defined instructions like CPUs; instead, you configure the hardware directly to perform tasks efficiently.

Basic Building Blocks of an FPGA

To understand how an FPGA works, it’s important to know its basic components. FPGAs are made up of:

Programmable Logic Blocks (PLBs): These are the “brains” of the FPGA, where the logic functions are implemented.

Interconnects: These are the wires that connect the logic blocks.

Input/Output (I/O) blocks: These allow the FPGA to communicate with external devices.

These elements work together to create a flexible platform that can be customized for various applications.

Understanding Programmable Logic Blocks (PLBs)

The heart of an FPGA lies in its programmable logic blocks. These blocks contain the resources needed to implement logic functions, which are essentially the basic operations of any electronic circuit. In an FPGA, PLBs are programmed using hardware description languages (HDLs) like VHDL or Verilog, enabling users to specify how the FPGA should behave for their particular application.

What are Look-Up Tables (LUTs)?

Look-Up Tables (LUTs) are a critical component of the PLBs. Think of them as small memory units that can store predefined outputs for different input combinations. LUTs enable FPGAs to quickly execute logic operations by “looking up” the result of a computation rather than calculating it in real-time. This speeds up performance, making FPGAs efficient at performing complex tasks.

The Role of Flip-Flops in FPGA Architecture

Flip-flops are another essential building block within FPGAs. They are used for storing individual bits of data, which is crucial in sequential logic circuits. By storing and holding values, flip-flops help the FPGA maintain states and execute tasks in a particular order.

Routing and Interconnects: The Backbone of FPGAs

Routing and interconnects within an FPGA are akin to the nervous system in a human body, transmitting signals between different logic blocks. Without this network of connections, the logic blocks would be isolated and unable to communicate, making the FPGA useless. Routing ensures that signals flow correctly from one part of the FPGA to another, enabling the chip to perform coordinated functions.

Why are FPGAs So Versatile?

One of the standout features of FPGAs is their versatility. Whether you're building a 5G communication system, an advanced AI model, or a simple motor controller, an FPGA can be tailored to meet the exact requirements of your application. This versatility stems from the fact that FPGAs can be reprogrammed even after they are deployed, unlike traditional chips that are designed for one specific task.

FPGA Configuration: How Does It Work?

FPGAs are configured through a process called “programming” or “configuration.” This is typically done using a hardware description language like Verilog or VHDL, which allows engineers to specify the desired behavior of the FPGA. Once programmed, the FPGA configures its internal circuitry to match the logic defined in the code, essentially creating a custom-built processor for that particular application.

Real-World Applications of FPGAs

FPGAs are used in a wide range of industries, including:

Telecommunications: FPGAs play a crucial role in 5G networks, enabling fast data processing and efficient signal transmission.

Automotive: In modern vehicles, FPGAs are used for advanced driver assistance systems (ADAS), real-time image processing, and autonomous driving technologies.

Consumer Electronics: From smart TVs to gaming consoles, FPGAs are used to optimize performance in various devices.

Healthcare: Medical devices, such as MRI machines, use FPGAs for real-time image processing and data analysis.

FPGAs vs. ASICs: What’s the Difference?

FPGAs and ASICs (Application-Specific Integrated Circuits) are often compared because they both offer customizable hardware solutions. The key difference is that ASICs are custom-built for a specific task and cannot be reprogrammed after they are manufactured. FPGAs, on the other hand, offer the flexibility of being reconfigurable, making them a more versatile option for many applications.

Benefits of Using FPGAs

There are several benefits to using FPGAs, including:

Flexibility: FPGAs can be reprogrammed even after deployment, making them ideal for applications that may evolve over time.

Parallel Processing: FPGAs excel at performing multiple tasks simultaneously, making them faster for certain operations than CPUs or GPUs.

Customization: FPGAs allow for highly customized solutions, tailored to the specific needs of a project.

Challenges in FPGA Design

While FPGAs offer many advantages, they also come with some challenges:

Complexity: Designing an FPGA requires specialized knowledge of hardware description languages and digital logic.

Cost: FPGAs can be more expensive than traditional microprocessors, especially for small-scale applications.

Power Consumption: FPGAs can consume more power compared to ASICs, especially in high-performance applications.

Conclusion

Understanding FPGA architecture is crucial for anyone interested in modern electronics. These devices provide unmatched flexibility and performance in a variety of industries, from telecommunications to healthcare. Whether you're a tech enthusiast or someone looking to learn more about cutting-edge technology, FPGAs offer a fascinating glimpse into the future of computing.

[designwithmanish]

Generate Verilog code from FSM or block diagram

[programmingandengineering]

ECE3829 Lab 2: VGA Display Design

Required deliverables: Functionality demonstrated and signed off. Archived project and a single pdf of your Verilog modules submitted to canvas at time of sign-off. Lab report submitted to canvas by the deadline. Getting Started and Counter Tutorial: Before starting this lab, you may wish to complete the counter tutorial. It walks you through to following processes. How to generate and…

[govindhtech]

AMD Vivado Design Suite 2024.2: Versal SoCs Revolutionized

What Is AMD Vivado?

A collection of design tools for AMD adaptive SoCs and FPGAs is called AMD Vivado. It contains tools for place and route, design entry, synthesis, verification, and simulation.

AMD Vivado Design Suite

The 2024.2 version, which includes significant improvements for designing with AMD Versal adaptable SoCs, is now available.

AMD Vivado 2024.2 highlights

Improved Versal Adaptive SoC Design Flows for AMD.

Fast Place and Route for All Versal Devices

Improved Advanced Flow for Quick Compilation.

Routability and congestion optimization.

Enabling Top-Level RTL Flows

Makes it possible to use transceivers from the top-level RTL and Versal programmable network on chip (NoC).

Fast Boot of Processing System in Versal Devices​

Segmented setup for quick OS.

Startup that satisfies a range of boot-sequence needs.

Facilitating quicker design iterations and achieving your FMAX goals more rapidly

The design program for AMD adaptive SoCs and FPGAs is called AMD Vivado. Design Entry, Synthesis, Place and Route, and Verification/Simulation tools are among its components.

Discover how sophisticated capabilities in the Vivado design tools enable designers to more precisely estimate power for AMD adaptive SoCs and FPGAs while cutting down on compilation times and design cycles.

Benefits

AMD Vivado Meeting Fmax Targets

One of the most difficult stages of the hardware design cycle is reaching your FMAX objective in a high-speed design. Vivado has special capabilities that assist you close timing, such Intelligent Design Runs (IDR), Report QoR Assessment (RQA), and Report QoR Suggestions (RQS). By using RQA, RQS, and IDR, you may reach your performance targets in a matter of days rather than weeks, which will increase your productivity significantly.

AMD Vivado Faster Design Iterations

As developers troubleshoot their ideas and add new features, design iterations are typical. These iterations are frequently minor adjustments made to a tiny section of the design. Incremental compile and Abstract Shell are two essential technologies in the AMD Vivado Design Suite that drastically cut down on design iteration times.

AMD Power Design Manager

Early and precise power prediction is essential for informing important design choices when creating FPGA and adaptive SoCs. For big and complicated devices like the Versal and UltraScale+ families, Power Design Manager is a next-generation power estimating tool designed to enable precise power estimation early in the design process. This tool was created especially to give precise power estimates for devices that have a lot of complicated hard IP blocks.

Design Flows

Design Entry & Implementation

Design entry in conventional HDL, such as VHDL and Verilog, is supported by AMD Vivado. Additionally, it supports the IP Integrator (IPI), a graphical user interface-based tool that enables a Plug-and-Play IP Integration Design Environment.

For today’s sophisticated FPGAs and SOCs, Vivado offers the finest synthesis and implementation available, with integrated timing closure and methodology capabilities.

Users may confine their design, assess findings, and close timing with the aid of the UltraFast methodology report (report_methodology), which is accessible in Vivado’s default flow.

Verification and Debug

To guarantee the final FPGA implementation’s functionality, performance, and dependability, verification and hardware debugging are essential. Effective validation of design functionality is made possible by the verification elements of the Vivado tool. Its extensive debugging capabilities enable engineers to quickly identify and fix problems in intricate designs.

Dynamic Function eXchange

With Dynamic Function eXchange (DFX), designers may make real-time changes to specific parts of their designs. The remaining logic can continue to function as designers download partial bitstreams to their AMD devices. This creates a plethora of opportunities for real-time performance improvements and design modifications. Designers may cut power consumption, upgrade systems in real-time, and switch to fewer or smaller devices via Dynamic Function eXchange.

AMD Vivado Platform Editions

AMD Vivado Design Suite- Standard & Enterprise Editions 

AMD Vivado Design Suite Standard Edition is available for free download. The Enterprise Edition’s license options start at $2,995.

Features

Licensing Options

AMD Vivado Standard

You may download the AMD Vivado Standard Edition for free, giving you immediate access to its essential features and capabilities.

AMD Vivado Enterprise

All AMD devices are supported by the fully functional Vivado Enterprise Edition of the design suite.

Recommended System Memory

Each target device family’s average and maximum AMD Vivado Design Suite memory utilization. AMD advises allocating enough physical memory to handle periods of high consumption.

Remarks

The more LUT and CLB are used, the more memory is used. The following figures were calculated with an average LUT usage of around 75%.

The amount of memory used is strongly impacted by the magnitude and complexity of timing restrictions.

The following figures were produced on a single synthesis and implementation run using the AMD Vivado tools in programmed batch mode.

DFX flow may result in increased memory use.

These devices are not compatible with 32-bit computers.

Answer Record 14932 describes how to set up a Windows 32-bit computer to use 3 GB of RAM.

Operation System

The following operating systems are compatible with AMD’s x86 and x86-64 chip architectures.

Features

Support for Microsoft Windows.

10.0 1809, 1903, 1909, and 2004 are Windows updates.

Support for Linux.

7.4, 7.5, 7.6, 7.7, 7.8, and 7.9 for CentOS and RHEL 7.

CentOS/RHEL 8: 8.1, 8.2, 8.3.

LE SUSE: 12.4, 15.2.

Among Ubuntu’s LTS versions are 16.04.5, 16.04.6, 18.04.1, 18.04.2, and 18.04.3, 18.04.4 LTS, 20.04 LTS, and 20.04.1 LTS.

Read more on Govindhtech.com

[takshila1]

Learning ASIC Design Online to Advance a Rewarding Career

The need for qualified ASIC (Application-Specific Integrated Circuit) designers has skyrocketed in line with the fast technological changes. Designed to satisfy individuals driven to succeed in electronics and embedded systems, an ASIC design course provides a portal into the fascinating field of custom chip design. Unlike general-purpose integrated circuits, ASICs are specialist circuits tailored for a certain application. From consumer electronics to healthcare and automotive, these chips are very essential in devices of many kinds. Learning ASIC design gives engineers the technical tools they need to create customized solutions, hence providing interesting career routes in sectors in demand.

Essential Learning Materials for an ASIC Design Course

Usually covering both basic and advanced subjects, an ASIC design course combines theory with useful design methods. Starting with the foundations of digital design, students next explore hardware description languages (HDLs) such as Verilog and VHDL, which are important for specifying circuit behavior. To guarantee circuits satisfy high-performance criteria, the course moves through logic synthesis, functional verification, and timing analysis. Emphasizing practical laboratories, students get real-world experience working with instruments of industrial standard. This extensive course guarantees that students grasp the design process completely, therefore equipping them for the demanding requirements of ASIC development employment.

Online ASIC Design Training's advantages

Online ASIC design training has made it simpler than ever in recent years to gain these specialist abilities free from geographical restrictions. Online courses let students and professionals study at their speed by offering flexible scheduling. These classes are meant to fit working professionals, students, and even amateurs hoping to become ASIC designers. Online training offers a collaborative learning environment using interactive modules, live sessions, and forums. Expert advice and peer conversations help students create a dynamic environment that replicates real-world situations while keeping flexibility for their hectic lives.

Employment Prospectives and Professional Development Using ASIC Design Skills

Demand for ASIC designers is strong in many areas, but especially in tech-driven sectors such as IoT, 5G, and artificial intelligence. Businesses always want talented ASIC designers to provide effective, small-sized, high-performance processors. Completing an ASIC design course lets professionals work as physical design experts, verification engineers, and ASIC design engineers with employment paying attractive rates and opportunities for career development. Furthermore, given the growing complexity of digital goods, ASIC knowledge of new technologies is always in demand, so this ability is not only useful but also future-proof in a sector that is always changing.

Selecting the Correct Platform for ASIC Design Education

Achieving one's professional objectives depends on choosing the right platform to learn ASIC design. Prospective students should search for courses offering a theoretical background as well as real-world industry tool experience like Cadence, Synopsys, and Mentor Graphics. The learning process may be improved with thorough assistance via digital laboratories, lecture recordings, and Q&A sessions, among other online tools. Many online ASIC design training courses include certificates that enhance a candidate's profile and provide credibility, therefore helping them stand out to companies in a crowded employment market. Selecting a respectable course guarantees students' readiness for the expectations of the sector.

Conclusion

Following an ASIC design course—especially via online resources—opens a world of possibilities in integrated circuit design. Those with specific expertise and useful abilities may boldly join the market in fields dependent on high-performance, customized chips. For novices as well as seasoned experts, the adaptability of online ASIC design training lets students acquire industry-relevant knowledge from anywhere. Platforms like takshila-vlsi.com provide priceless training materials for people wanting to improve their VLSI abilities and flourish in ASIC design, therefore bridging the knowledge gap between expertise required in today's tech scene.

[lorryelectronicblog]

Understanding FPGA Architecture: Key Insights

Introduction to FPGA Architecture

Imagine having a circuit board that you could rewire and reconfigure as many times as you want. This adaptability is exactly what FPGAs offer. The world of electronics often seems complex and intimidating, but understanding FPGA architecture is simpler than you think. Let’s break it down step by step, making it easy for anyone to grasp the key concepts.

What Is an FPGA?

An FPGA, or Field Programmable Gate Array, is a type of integrated circuit that allows users to configure its hardware after manufacturing. Unlike traditional microcontrollers or processors that have fixed functionalities, FPGAs are highly flexible. You can think of them as a blank canvas for electrical circuits, ready to be customized according to your specific needs.

How FPGAs Are Different from CPUs and GPUs

You might wonder how FPGAs compare to CPUs or GPUs, which are more common in everyday devices like computers and gaming consoles. While CPUs are designed to handle general-purpose tasks and GPUs excel at parallel processing, FPGAs stand out because of their configurability. They don’t run pre-defined instructions like CPUs; instead, you configure the hardware directly to perform tasks efficiently.

Basic Building Blocks of an FPGA

To understand how an FPGA works, it’s important to know its basic components. FPGAs are made up of:

Programmable Logic Blocks (PLBs): These are the “brains” of the FPGA, where the logic functions are implemented.

Interconnects: These are the wires that connect the logic blocks.

Input/Output (I/O) blocks: These allow the FPGA to communicate with external devices.

These elements work together to create a flexible platform that can be customized for various applications.

Understanding Programmable Logic Blocks (PLBs)

The heart of an FPGA lies in its programmable logic blocks. These blocks contain the resources needed to implement logic functions, which are essentially the basic operations of any electronic circuit. In an FPGA, PLBs are programmed using hardware description languages (HDLs) like VHDL or Verilog, enabling users to specify how the FPGA should behave for their particular application.

What are Look-Up Tables (LUTs)?

Look-Up Tables (LUTs) are a critical component of the PLBs. Think of them as small memory units that can store predefined outputs for different input combinations. LUTs enable FPGAs to quickly execute logic operations by “looking up” the result of a computation rather than calculating it in real-time. This speeds up performance, making FPGAs efficient at performing complex tasks.

The Role of Flip-Flops in FPGA Architecture

Flip-flops are another essential building block within FPGAs. They are used for storing individual bits of data, which is crucial in sequential logic circuits. By storing and holding values, flip-flops help the FPGA maintain states and execute tasks in a particular order.

Routing and Interconnects: The Backbone of FPGAs

Routing and interconnects within an FPGA are akin to the nervous system in a human body, transmitting signals between different logic blocks. Without this network of connections, the logic blocks would be isolated and unable to communicate, making the FPGA useless. Routing ensures that signals flow correctly from one part of the FPGA to another, enabling the chip to perform coordinated functions.

Why are FPGAs So Versatile?

One of the standout features of FPGAs is their versatility. Whether you're building a 5G communication system, an advanced AI model, or a simple motor controller, an FPGA can be tailored to meet the exact requirements of your application. This versatility stems from the fact that FPGAs can be reprogrammed even after they are deployed, unlike traditional chips that are designed for one specific task.

FPGA Configuration: How Does It Work?

FPGAs are configured through a process called “programming” or “configuration.” This is typically done using a hardware description language like Verilog or VHDL, which allows engineers to specify the desired behavior of the FPGA. Once programmed, the FPGA configures its internal circuitry to match the logic defined in the code, essentially creating a custom-built processor for that particular application.

Real-World Applications of FPGAs

FPGAs are used in a wide range of industries, including:

Telecommunications: FPGAs play a crucial role in 5G networks, enabling fast data processing and efficient signal transmission.

Automotive: In modern vehicles, FPGAs are used for advanced driver assistance systems (ADAS), real-time image processing, and autonomous driving technologies.

Consumer Electronics: From smart TVs to gaming consoles, FPGAs are used to optimize performance in various devices.

Healthcare: Medical devices, such as MRI machines, use FPGAs for real-time image processing and data analysis.

FPGAs vs. ASICs: What’s the Difference?

FPGAs and ASICs (Application-Specific Integrated Circuits) are often compared because they both offer customizable hardware solutions. The key difference is that ASICs are custom-built for a specific task and cannot be reprogrammed after they are manufactured. FPGAs, on the other hand, offer the flexibility of being reconfigurable, making them a more versatile option for many applications.

Benefits of Using FPGAs

There are several benefits to using FPGAs, including:

Flexibility: FPGAs can be reprogrammed even after deployment, making them ideal for applications that may evolve over time.

Parallel Processing: FPGAs excel at performing multiple tasks simultaneously, making them faster for certain operations than CPUs or GPUs.

Customization: FPGAs allow for highly customized solutions, tailored to the specific needs of a project.

Challenges in FPGA Design

While FPGAs offer many advantages, they also come with some challenges:

Complexity: Designing an FPGA requires specialized knowledge of hardware description languages and digital logic.

Cost: FPGAs can be more expensive than traditional microprocessors, especially for small-scale applications.

Power Consumption: FPGAs can consume more power compared to ASICs, especially in high-performance applications.

Conclusion

Understanding FPGA architecture is crucial for anyone interested in modern electronics. These devices provide unmatched flexibility and performance in a variety of industries, from telecommunications to healthcare. Whether you're a tech enthusiast or someone looking to learn more about cutting-edge technology, FPGAs offer a fascinating glimpse into the future of computing.

[codeshive]

CECS 225 LAB 02: Simple Logic Function F = A(B+C+D’) solved

Objectives: Continue to get familiar with EDAPlayground Similar to the tutorial/lab01, this project asks you to repeat the same procedures to create Verilog module, to write testbench code, and to generate the simulation waveform for the above given logic function. Things needed to turn in (combine everything into a single word file) Truth table/Function Talbe for function F showing all…

[codingprolab]

ECE5724 Homework 4: Scan insertion and scan testing by Verilog virtual tester

Description: In this homework, you are provided with the netlist of the SSC circuit (netlist_SSC_V1) that you got familiar with in homework 1. In addition, the equivalent netlist in the .bench format is created (SSC.bench). You are to: 1- Unfold “SSC.bench” file and separate the combinational part as discussed in the course lectures. 2- Apply Atalanta to the unfolded file to generate a good test…

[myprogrammingsolver]

ECE3829 Lab 2: VGA Display Design

Required deliverables: Functionality demonstrated and signed off. Archived project and a single pdf of your Verilog modules submitted to canvas at time of sign-off. Lab report submitted to canvas by the deadline. Getting Started and Counter Tutorial: Before starting this lab, you may wish to complete the counter tutorial. It walks you through to following processes. How to generate and…

[zannatykhatun]

Bard welcomes a big update: finally supports Chinese!

With both ChatGPT and Claude ushering in major updates, Bard, owned by Google, is finally not far behind and has released a new version. However, compared to ChatGPT’s artifact Code interpreter, Bard’s updates further improve Bard’s user experience.

However, for Chinese users, this update is of great significance: because Bard finally adds support for Chinese this time.

1. 40 new languages ​​added, Chinese conversation is stress-free

Bard has added more than 40 new  HE Tuber languages ​​this time, including Chinese, Arabic, German, Hindi and Spanish.

The Silicon Star man immediately chatted with Bard. Of course Bard answered some common questions fluently. Take a "Chinese Level 10" question and test it to see if it understands the breadth and depth of Chinese:

Not a bad answer.

But when the Silicon Stars asked it to write a seven-character quatrain, Bard overturned:

2. More new experiences

In addition, Bard also adds voice support. The new version of Bard adds a small loudspeaker icon. Click it and you can hear Bard read out the answer. This is especially useful for users who want to hear the correct pronunciation of a word or listen to a poem or script. This feature now supports more than 40 languages, and Chinese is also supported.

Additionally, users can easily adjust Bard's answers. Users can now change the tone and style of Bard's answers to five different options: simple, long, short, professional or casual. For example, if you think Bard's answer is too long, you can use the drop-down menu to shorten it. Currently this feature only supports English.

At the I/O conference, Google announced that it would bring the functionality of Google Lens to Bard. This update implements the integration of Google Lens. Users can now upload images with prompts, and Bard will analyze the image content and information to provide help. This feature is also currently only available in English.

Source: Twitter

In addition, this Bard has also made some adjustments at the product level.

Pin and rename conversations: Options to pin, rename, and select recent conversations are now available in the sidebar, making it easier for users to revisit these prompts later.

Export code to more places: In addition to Google Colab, users are also allowed to export Python code to Replit.

Share replies with friends: Shareable links allow users to share ideas and creations with others.

3. What is the strength of the Palm 2 behind Bard?

Google's Bard is trained based on its own PaLM 2 model.

The first generation PaLM is a large language model announced by Google in April 2022. It uses 540 billion parameters for training, which is about three times that of GPT-3. The new version of PaLM 2 has been further improved and improved on PaLM, with multi-language, reasoning and coding functions.

PaLM 2 has more training in multilingual texts, covering more than 100 languages, and has a remarkable ability to understand, generate and translate nuanced texts including idioms, poetry and riddles, and also passes "Mastery" Level advanced language proficiency test.

In terms of reasoning, PaLM 2's data set includes scientific papers and web pages with mathematical expressions, and it has strong logic, common sense reasoning and mathematical abilities.

At the same time, PaLM2 is pre-trained on a large number of public source code data sets, and its coding ability is stronger. In addition to Python and JavaScript, it also includes generating specialized code in Prolog, Fortran and Verilog.

It is worth noting that PaLM 2 has been developed in different versions, which can be targeted at different customers and deployed in different enterprise environments.

Currently, PaLM 2 has four specifications, ranging from small to large: Gecko, Otter, Bison and Unicorn. Among them, the smallest Gecko can run on a mobile phone and can process approximately 20 Tokens per second, which is about 16 or 17 word. In other words, developers do not need to spend a lot of time and resources to create and adjust PaLM 2, but can directly use it and deploy it.

However, in terms of Bard's current performance, it is always at least one step behind ChatGPT. ChatGPT's Code Interpreter plug-in is finally fully online. Many people say that Code Interpreter is GPT 4.5 wearing a plug-in mask. This Bard update, in addition to In addition to the support for Chinese that makes Chinese users excited, there are not many other surprises.

When will Bard be able to use his bigger moves?

Author: VickyXiao, Juny; Editor: VickyXiao

Original title: Bard welcomes a big update: finally supports Chinese! Go and "tease" it

Source public account: Silicon Star (ID: guixingren123), from technology to culture, from depth to jokes, Silicon Star will tell you everything about Silicon Valley.

This article is published with the authorization of Product Manager cooperative media @PINWAN. Reprinting without permission is prohibited.

The title image is from Unsplash and is licensed under CC0.

The opinions in this article represent only the author's own. The Renren Product Manager platform only provides information storage space services.

[programmingandengineering]

ECE3829 Lab 2: VGA Display Design

Required deliverables: Functionality demonstrated and signed off. Archived project and a single pdf of your Verilog modules submitted to canvas at time of sign-off. Lab report submitted to canvas by the deadline. Getting Started and Counter Tutorial: Before starting this lab, you may wish to complete the counter tutorial. It walks you through to following processes. How to generate and…

[govindhtech]

Intel’s Silicon Mobility OLEA U310 SoC Boosts EV Progress

Silicon Mobility OLEA U310

One of the main obstacles to purchasing an electric vehicle (EV) is still its expensive cost, which deters many prospective consumers worldwide. Due in large part to the high expense of developing improved battery and e-motor technologies, electric vehicles (EVs) are now more expensive to construct than conventional gasoline-powered vehicles. Improving the efficiency of current battery technology at the vehicle level through energy savings and better interaction with EV station infrastructure is the short-term solution.

With the release of the new OLEA U310 system-on-chip (SoC) today, Silicon Mobility, an Intel company, has successfully addressed this precise difficulty. The entire performance of electric cars (EVs) will be greatly enhanced by this next-generation technology, which will also expedite the design and production processes and expand SoC services to guarantee smooth operation across a variety of EV station platforms.

Mobility in Silicon

The new SoC, which is a first for the industry, is the first all-in-one solution that combines software and hardware, and it is designed to meet the requirements of distributed software-based electrical architectures for powertrain domain control. With its distinct hybrid and heterogeneous architecture, the OLEA 310 FPCU can take the place of up to six conventional microcontrollers in a system configuration that includes an on-board charger, a gearbox, an inverter, a motor, and a DC-DC converter. Original equipment manufacturers (OEMs) and Tier 1 suppliers can regulate a variety of power and energy functions simultaneously and in real time with the 310 FPCU.

Create a function grouping for your e-powertrain

The OLEA U310 is a recent addition to the Silicon Mobility FPCU line. Its design matches distributed software requirements for powertrain domain control in electrical/electronic designs. Beyond the capabilities of conventional microcontrollers, the OLEA U310 is constructed with a novel hybrid and heterogeneous architecture that embeds numerous software and hardware programmable processing and control units seamlessly integrating functional safety and the cybersecurity into its fundamental design. It hosts and connects, on a single chip, the essential event-based multifunction control requirements with the time-based and multitask software application needs.

Created with the newest demands in automobile control in mind

The OLEA U310 can do more than only powertrain tasks. Additional uses for this adaptable system-on-a-chip include:

Systems for Chassis Control

Fusion of Data

Compressor air

System for Thermal Management

Different Control Mechanisms

EV makers may create a more integrated and effective control system that improves control and performance by utilising the adaptability of the OLEA U310.

Authority of the AxEC

For direct sensor and actuator interfacing, the Advanced eXecution & Event Control (AxEC) unit integrates programmable hardware, mathematical coprocessors, and adjustable peripherals. The core of the FPCU architecture is the programmable hardware known as the Flexible Logic Units (FLU). It is a programmable logic fabric that can be designed using common hardware description languages like Verilog or VHDL. It is furnished with flip-flops, SRAM, lookup tables, and signal processing units. 1-4 FLU partitions are a notion that is introduced by the OLEA U Series.

CPUs are in charge of high-level and low-response-time software, while AxEC deals with real-time control and fast-response processing. For particular jobs, designers have the option of using CPU or AxEC; nevertheless, AxEC usually performs sophisticated processing, minimising CPU utilisation. Regardless of the number or frequency of events, hardware processing guarantees prompt, accurate responses.

Protected by OLEA SiLant

The greatest level of automotive safety integrity specified by the ISO 26262 functional safety standard, ASIL-D design ready, is met by the FPCU. The OLEA U Series Safety Integrity Agent (SiLant) is in charge of identifying, containing, and responding to errors in nanoseconds. It is the key hub for all safety measures integrated within the FPCU. SiLant detects software and system faults in addition to latent and transient faults at the semiconductor level.

OLEA U FLU provides safe multitasking and function grouping with unified firmware virtualization from CPU down to FLU level with the advent of multi-CPU and multi-FLU. OLEA U offers assurances and a deterministic architecture. Worst-Scene Performance It’s time to create applications that require safety.

Protected by OLEA FHSM

For the best defence against current and potential threats, the latest generation of FPCU is available. A subsystem integrated into the OLEA U Series that complies with the ISO 21434 automotive cybersecurity standard and EVITA Full is called the Flexible Hardware Security Module (FHSM). Its specialised programmable hardware allows it to contain hardware-accelerated security functions that can be used to improve protection or keep an eye out for any system security breaches. This special feature makes use of a wider range of cryptographic techniques to enable safe real-time communications as well as secure software updates and execution.

Mobility of Silicon

Together with the bill of material (BoM) reduction, preliminary data indicates that compared to current EVs, there will be a 5% increase in energy efficiency, a 25% reduction in motor size for the same power, a 35% decrease in cooling requirements, and a 30% reduction in passive component size. With fewer components to incorporate, the new Silicon Mobility technology enables EV makers to develop software-defined electric vehicles with superior performance, increased range, and potentially cheaper production costs. The industry’s transition to an all-electric and software-defined future will be accelerated by the new solution, which also enhances Intel Automotive’s current line of AI-enhanced software-defined vehicle (SDV) SoCs.

Silicon Mobility OLEA U310 Features

2nd generation of FPCU

3x Cortex-R52 @ 350MHz – 2196 DMIPS

AxEC 2.0: 2x FLUs @ 175Mhz – 400 GOPS + 9.1 GMAC

SILant 2.0: Safe and Determinist Multi-Core/FLU

Flexible HSM: HW & SW EVITA Full

8MB of P-Flash, 256kB of D-Flash, 1MB of SRAM

CAN FD, CAN XL, Ethernet

ISO/SAE 21434 certifieISO 26262 ASIL-D & ISO/SAE 21434 compliant

AEC-Q100 Grade 1

292 BGA

Read more on Govindhtech.com

[takachan]

PaLM 2 のご紹介

過去 10 年間の AI における最大の進化を振り返ると、Google はその多くの場面で先頭に立ってきました。私たちの基盤モデルにおける画期的な研究は、この業界と何十億人もの人々が毎日使用している AI を活用した製品の基礎になっています。私たちがこれらの技術を責任を持って進歩させ続けることで、医療から人間の創作活動に至るまで、広範囲にわたる分野で大きな変革をもたらすことができる可能性があります。 AI の開発における過去 10 年間に、私たちはニューラルネットワークを大規模化することで非常に多くのことが可能になることを学びました。実際、より大きなサイズのモデルが驚くべき能力を示すのを見てきました。しかし同時に、私たちの研究を通じて、それは「大きければ良い」という単純なものではなく、創造的な研究が優れたモデルを構築するための鍵であることも学びました。モデルのアーキテクチャと学習方法に関する最近の進歩の過程で、マルチモーダル性を獲得させる方法、人間からのフィードバックをプロセスに含めることの重要性、モデルをこれまで以上に効率的に構築する方法などが分かりました。これらはどれも有用な技術要素であり、AI の最新技術を進歩させ、人々の日常生活に真の利益をもたらすモデルを構築していくために役立ちます。 PaLM 2 について 本日、次世代言語モデルである PaLM 2 を発表しました。PaLM 2 は、この研究を発展させたもので、多言語、推論、およびコーディング機能が向上した最先端の言語モデルです。 多言語：PaLM 2 は、100 以上の言語にわたる多言語テキストに、より重点をおいて学習しています。これにより、慣用句、詩、なぞなぞなど、ニュアンスを含む表現を数多くの言語で理解、生成、翻訳するという難しい問題に対する性能が大幅に向上しました。PaLM 2 は、上級レベルの言語能力試験で「習得」レベルに合格しました。 推論：PaLM 2 で学習に利用した広範なデータセットには、数式を含む科学論文やウェブページが含まれています。その結果、ロジック、常識に基づく推論、数学に関する能力が向上しています。 コーディング（プログラミング）：PaLM 2 は、公開されている大量のソースコードデータセットで事前学習しています。結果として、Python や JavaScript などのよく使われるプログラミング言語だけでなく、Prolog、Fortran、Verilog などの言語でコードを生成することもできます。 幅広い用途をカバーするモデルラインナップ PaLM 2 は、以前のモデルよりも能力が高く、高速で効率的です。また、さまざまなサイズで提供されるため、あらゆる用途で展開できます。 PaLM 2 には、Gecko、Otter、Bison、Unicorn という 4 つのサイズがあります。Gecko は非常に軽量なため、モバイル 端末で動作し、オフラインの状態でも、デバイス上の優れたインタラクティブ　アプリケーションを実現するのに十分な速さです。この汎用性のおかげで、PaLM 2 は、幅広い製品をカバーする形でファインチューニングすることができ、より多様な形でより多くの方の役に立つことができます。 25 を超える Google の製品と機能に搭載 本日の I/O で、PaLM 2 を搭載した 25 以上の製品と新機能を発表しました。PaLM 2 は、最新の高度な AI 機能を直接製品に搭載し、世界中のユーザー、開発者、企業などあらゆる規模の人々に提供します。以下にいくつかの例を挙げます。 * PaLM 2 の多言語機能の向上により、本日より Bard を日本語を含む新しい言語で対応しました。また、最近発表されたコーディングアップデートにも利用されています。 * Gmail と Google ドキュメントでの下書きやスプレッドシートでの整理をサポートする Google Workspace の機能は、PaLM 2 の機能を活用することで、ユーザーがより良い仕事をより速く行えるよう支援します。 * 当社の医療研究チームが医療情報のデータを使って学習した Med-PaLM 2 は、理解するのが容易でない医学文章の内容を用いて質問に答えたり、洞察をまとめたりすることができます。医師としての適性を測るテストにおいて最先端の結果を達成し、米国医師国家試験形式の質問で「エキスパート」レベルのパフォーマンスを達成した最初の大規模言語モデルとなりました。現在、X 線やマンモグラフィーなどの情報も統合するためのマルチモーダル機能を追加しており、将来的に患者の回復に貢献することを目指しています。Med-PaLM 2 は、安全で役立つ利用法を探すため、今年の夏後半に少数の Cloud のお客様にフィードバックをいただくことを目的とし提供する予定です。 * Sec-PaLM は、セキュリティユースケース向けにトレーニン��された PaLM 2 の特殊バージョンであり、サイバーセキュリティ分析の飛躍的な可能性を秘めています。Google Cloud を通じて提供される Sec-PaLM は、AI を利用して潜在的に悪意のあるスクリプトの動作を分析・説明することで、人や組織に実際に脅威を与えるスクリプトをかつてないほど迅速に検出できます。 * この 3 月から、少数の開発者向けに PaLM API のプレビューを実施しています。本日より、開発者は PaLM 2 モデルへの登録が可能になり、お客様は Vertex AI でエンタープライズクラスのプライバシー、セキュリティ、ガバナンスを備えたモデルを使用することができます。PaLM 2 は Duet AI for Google Cloud にも搭載されています。Duet AI は、ユーザーがこれまで以上に速く学び、構築し、運用できるように設計されたジェネレーティブ AI とのコラボレーションです。 * Google 検索の Search Labs では、PaLM2 や さらに進化した MUM など複数の大規模言語モデルをベースとしたジェネレーティブ AI を活用した SGE (Search Generative Experience) の試験運用を行います。SGEは、まずは英語での対応となり、米国にて登録された方のみお試しいただけます。 AI の未来を前進させる PaLM 2 は、非常に高性能なモデルが多様な大きさと速度で提供されることで、多用途に使えるのAIモデルがすべての人にとって真の利益をもたらすことを示してくれました。Google は現在、最も役立ち責任ある AI ツールの提供に尽力しているのと同時に、Google にとってこれまでで最高の基盤モデルの作成にも取り組んでいます。 Google の Brain と DeepMind の研究チームは、過去 10 年間に AI で多くの画期的な成果を生み出してきました。この 2 つのワールドクラスのチームを 1 つのユニットに統合し、進歩を加速し続けています。Google の計算リソースに支えられた Google DeepMind は、毎日使用する製品に驚くべき新機能をもたらすだけでなく、責任を持って次世代の AI モデルへの道を開くでしょう。 私たちはすでに、Gemini の開発に取り組んでいます。Gemini は、マルチモーダル、そして他のツールや API との統合に効率的でメモリやプランニングなどのイノベーションを実現するために一から構築している次世代モデルです。Gemini はまだ学習中ですが、以前のモデルでは見られなかったレベルでのマルチモーダル能力をすでに示しています。Gemini は、安全性の観点でのファインチューニングとテストを行った上で、PaLM 2 と同様にさまざまなサイズと性能として提供し、あらゆる人が恩恵を受けられるように多様な製品、アプリケーション、およびデバイスに展開する予定です。   Posted by Zoubin Ghahramani, Vice President, Google DeepMind http://japan.googleblog.com/2023/05/palm-2.html?utm_source=dlvr.it&utm_medium=tumblr Google Japan Blog

[techav]

SE-VGA

I've started a new project.

Inspired by recent work on creating modern reproductions of the Mac SE logic board and following my previous CPLD VGA generator project, I've been working on a PDS card for the Mac SE that mirrors its video on a VGA monitor.

I'm using a similar approach to the [bbraun] project, which used an stm32f4 to watch the SE's CPU bus for writes to the SE frame buffer memory addresses. Instead of using a microcontroller I'm using an Atmel ATF1508AS CPLD to monitor the SE CPU bus for writes to the frame buffer addresses and storing the data in a pair of 32kB SRAM chips. The CPLD then reads back the video data to generate a 640x480 monochrome VGA signal with the SE video in letterboxed in its original 512x342 resolution.

The circuit itself is fairly straightforward. The CPLD runs everything off a single clock signal from a can oscillator, and uses the pair of SRAM chips for video memory. Other than those four chips, there are a few passive components. It's simple enough I could have built one with point-to-point wiring or even wire-wrap. But, to reduce debugging and the potential for noise disrupting the SE's normal operation, I decided to lay out and order some small PCBs.

I got these from JLC for $2 plus shipping and they arrived in just under two weeks. Build was easy enough. I used the drag solder technique with a lot of flux to solder on the 100 pin QFP package CPLD and it went on with no problems. Everything else is through-hole.

I tried to take a methodical approach to build and debug. I started with just the CPLD and clock to make sure it could generate a proper sync signal that was recognized by my monitor. That much worked without issue, so I moved on to testing if it could read data from its VRAM bus and display it. This part took some work with a logic analyzer and a few rounds of updates, but eventually I was able to tie one VRAM Data pin high and get it to display lines.

From there, I added the VRAM sockets to test if it could properly read from VRAM and display its contents. SRAM powers on to random contents, and when I added the SRAM to the board and powered it on, I was greeted with a screen of random pixels. VRAM was working, and the video generator was displaying a stable, consistent image.

At this point there was only one thing left to do — solder on the (expensive!) DIN 41612 connector and test it out in the SE.

Well, it was half working.

I had half of the image on screen, so it was clearly recognizing CPU write cycles, storing it in VRAM, and recalling it in sequence. I quickly found and corrected a bug in the code looking for the 68000 lower data strobe (!LDS), and inverted the final output and tried again.

It ... Wasn't quite right. It started out fine while Mac OS was booting. There was a little noise in the image, but not too bad ... until it reached the Finder. By the time it was finished booting, it was flashing, alternating between valid video data and a garbage data. The garbage data seemed to be encroaching on the valid data as well, the longer the system ran.

The first bug didn't take long to find. The classic Macs, including the SE actually support double-buffered video. They have a primary frame buffer and an alternate frame buffer, selected by setting or clearing one output bit on the VIA chip. I designed the card to support both frame buffers, and to also watch the CPU bus for writes to the specific VIA bit that controls frame buffer selection. I had calculated the VIA address wrong though, so it was swapping between frame buffers when it shouldn't have and that's what was causing the flashing.

I still had the problem of garbage data being displayed however. This one took a while to figure out, and I'm actually still not sure how it was happening to begin with.

The logic analyzer showed that every so often a VRAM write cycle would overlap with a VRAM read cycle. The VRAM write state machine shouldn't have allowed that to happen, but it was. Unable to find anything that would cause the cycles to overlap, o added a test to delay the write cycle if it detected a current read cycle.

The result?

No more garbage data.

I really can't believe it. I wasn't sure I could get this to work, and I wasn't sure it would fit into a CPLD with only 128 macrocells. To top it off, this is my first real project using System Verilog instead of VHDL.

It's not perfect yet. There is one column of garbage data being displayed on the left of the image, and it looks like the last column off the right is also ending up on the left. But, it is completely useable.

I'm not finished with this project yet. I want to bump it up to XGA resolution (1024x768), which would allow the SE video to be pixel doubled and take up more of the screen. The 65MHz clock necessary for XGA is hard to come by, so I'm thinking about spinning up a rev 2 board that uses an FPGA instead of a small CPLD.

This has been a fun project. It's always so exciting for a project to have visible results.

I have the project on GitHub if anyone is interested in taking a closer look.