What’s So Important About Processor Extensibility?


While the ability to extend a processor is nothing new, market dynamics are forcing a growing percentage of the industry to consider it a necessary part of their product innovation. From small IoT functions to massive data centers and artificial intelligence, the need to create an optimized processing platform is often the only way to get more performance or lower power out of the silicon area ... » read more

What Machine Learning Can Do In Fabs


Semiconductor Engineering sat down to discuss the issues and challenges with machine learning in semiconductor manufacturing with Kurt Ronse, director of the advanced lithography program at Imec; Yudong Hao, senior director of marketing at Onto Innovation; Romain Roux, data scientist at Mycronic; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation. L-R:... » read more

How Much Power Will AI Chips Use?


AI and machine learning have voracious appetites when it comes to power. On the training side, they will fully utilize every available processing element in a highly parallelized array of processors and accelerators. And on the inferencing side they, will continue to optimize algorithms to maximize performance for whatever task a system is designed to do. But as with cars, mileage varies gre... » read more

Software In Inference Accelerators


Geoff Tate, CEO of Flex Logix, talks about the importance of hardware-software co-design for inference accelerators, how that affects performance and power, and what new approaches chipmakers are taking to bring AI chips to market. » read more

Ensuring Coverage In Large SoCs


Sven Beyer, product manager for design verification at OneSpin Solutions, talks about why formal technology is required to ensure coverage in some of the newest chips, how it deals with potential interactions and different use cases, and why it is gaining traction in automotive applications. » read more

Plasticine: A Reconfigurable Architecture For Parallel Patterns (Stanford)


Source: Stanford University Stanford University has been developing Plasticine, which allows parallel patterns to be reconfigured. "ABSTRACT Reconfigurable architectures have gained popularity in recent years as they allow the design of energy-efficient accelerators. Fine-grain fabrics (e.g. FPGAs) have traditionally suffered from performance and power inefficiencies due to bit-level ... » read more

More Knobs, Fewer Markers


The next big thing in chip design may be really big — the price tag. In the past, when things got smaller, so did the cost per transistor. Now they are getting more expensive to design and manufacture, and the cost per transistor is going up along with the number of transistors per area of die, and in many cases even the size of the die. That's not exactly a winning economic formula, which... » read more

Uses And Limitations Of AI In Chip Design


Raik Brinkmann, president and CEO of OneSpin Solutions, sat down with Semiconductor Engineering to talk about AI changes and challenges, new opportunities for using existing technology to improve AI, and vice versa. What follows are excerpts of that conversation. SE: What's changing in AI? Brinkmann: There are a couple of big changes underway. One involves AI in functional safety, where y... » read more

Things That Go Bump In The Daytime


There is no argument that autonomous technology is better at certain things than systems controlled by people. A computer-guided system has only one mission — to stay on the road, avoid object, and reach the end destination. It doesn't get tired, text, or look out the window. And it can park within a millimeter of a wall or another vehicle without hitting it, and do that every time — as lon... » read more

What Will AI Look Like In 10 Years?


There's no such thing as reverse in AI systems. Once they are let loose, they do what they were programmed to do — optimize results within a given set of parameters. But today there is no consistency for those parameters. There are no standards by which to measure how AI deviates over time. And there is an expectation, at least today, that AI systems will adapt to whatever patterns they di... » read more

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