tag: machine learning

Powering The Edge: Driving Optimal Performance With the Arm ML Processor
By Arm - 07 Aug, 2019 - Comments: 0

On-device machine learning (ML) processing is already happening in more than 4 billion smart phones. As the adoption of connected devices continues to grow exponentially, the resulting data explosion means cloud processing could soon become an expensive and high-latency luxury. The Arm ML processor is defining the future of ML inference at the edge, allowing smart devices to make independent... » read more

Process Window Optimization
By Ed Sperling - 07 Aug, 2019 - Comments: 0

David Fried, vice president of computational products at Lam Research, examines increasing process variation and interactions between various types of variation, why different approaches are necessary to improve yield and continue scaling. » read more

The Great Test Blur
By Ed Sperling - 06 Aug, 2019 - Comments: 0

As chip design and manufacturing shift left and right, concerns over reliability are suddenly front and center. But figuring out what exactly what causes a chip to malfunction, or at least not meet specs for performance and power, is getting much more difficult. There are several converging trends here, each of which plays an integral role in improving reliability. But how significant a role... » read more

Hardware-Software Co-Design Reappears
By Brian Bailey - 25 Jul, 2019 - Comments: 1

The core concepts in hardware-software co-design are getting another look, nearly two decades after this approach was first introduced and failed to catch on. What's different this time around is the growing complexity and an emphasis on architectural improvements, as well as device scaling, particularly for AI/ML applications. Software is a critical component, and the more tightly integrate... » read more

Power Is Limiting Machine Learning Deployments
By Brian Bailey - 25 Jul, 2019 - Comments: 3

The total amount of power consumed for machine learning tasks is staggering. Until a few years ago we did not have computers powerful enough to run many of the algorithms, but the repurposing of the GPU gave the industry the horsepower that it needed. The problem is that the GPU is not well suited to the task, and most of the power consumed is waste. While machine learning has provided many ... » read more

Say Welcome to the Machine: Low-Power Machine Learning for Smart IoT Applications
By Synopsys - 24 Jul, 2019 - Comments: 0

By Pieter van der Wolf, Principal R&D Engineer, Synopsys Inc. and Dmitry Zakharov, Senior Software Engineer, Synopsys Inc Smart IoT devices that interact intelligently with their users are appearing in many application areas. Increasingly, these devices apply machine learning technology for processing captured sensor data, so that smart actions can be taken based on recognized patterns. ... » read more

Using Memory Differently To Boost Speed
By Ed Sperling - 22 Jul, 2019 - Comments: 2

Boosting memory performance to handle a rising flood of data is driving chipmakers to explore new memory types and different ways of using existing memory, but it also is creating some complex new challenges. For most of the semiconductor design industry, memory has been a non-issue for the past couple of decades. The main concerns were price and size, but memory makers have been more than a... » read more

GDDR Accelerates Artificial Intelligence And Machine Learning
By Frank Ferro - 16 Jul, 2019 - Comments: 0

The origins of modern graphics double data rate (GDDR) memory can be traced back to GDDR3 SDRAM. Designed by ATI Technologies, GDDR3 made its first appearance in NVidia’s GeForce FX 5700 Ultra card which debuted in 2004. Offering reduced latency and high bandwidth for GPUs, GDDR3 was followed by GDDR4, GDDR5, GDDR5X and the latest generation of GDDR memory, GDDR6. GDDR6 SGRAM supports a ma... » read more

Low-Power Design Becomes Even More Complex
By Ann Mutschler - 16 Jul, 2019 - Comments: 0

Throughout the SoC design flow, there has been a tremendous amount of research done to ease the pain of managing a long list of power-related issues. And while headway has been made, the addition of new application areas such as AI/ML/DL, automotive and IoT has raised as many new problems as have been solved. The challenges are particularly acute at leading-edge nodes where devices are power... » read more

Will In-Memory Processing Work?
By Brian Bailey - 16 Jul, 2019 - Comments: 1

The cost associated with moving data in and out of memory is becoming prohibitive, both in terms of performance and power, and it is being made worse by the data locality in algorithms, which limits the effectiveness of cache. The result is the first serious assault on the von Neumann architecture, which for a computer was simple, scalable and modular. It separated the notion of a computatio... » read more

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