Speeding Up AI With Vector Instructions


A search is underway across the industry to find the best way to speed up machine learning applications, and optimizing hardware for vector instructions is gaining traction as a key element in that effort. Vector instructions are a class of instructions that enable parallel processing of data sets. An entire array of integers or floating point numbers is processed in a single operation, elim... » read more

Zeroing In On Biological Computing


Artificial spiking neural networks need to replicate both excitatory and inhibitory biological neurons in order to emulate the neural activation patterns seen in biological brains. Doing this with CMOS-based designs is challenging because of the large circuit footprint required. However, researchers at HP Labs observed that one biologically plausible model, the Hodgkins-Huxley model, is math... » read more

The Very Long Road To Autonomous Vehicles


It may be a long wait before fully autonomous vehicles hit the road. Even semi-autonomous vehicles aren't doing so well. The American Automobile Association drove 4,000 miles in cars equipped with active driver assistance, averaging problems every 8 miles. AAA cited a host of problems, including driving too close to other cars or guardrails, aggressive braking, and automated steering that wo... » read more

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

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