Neural Networks Without Matrix Math


The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. The network first analyzes a training example, typically assign... » read more

A Historical Case For Precision


We take for granted today the staggering precision of modern technology. Cars, electronics, robots, and medical equipment all come off the factory floor composed of effortlessly interchangeable parts, but this was not always the case. In the late 18th century most things that required any kind of precision were made by hand, one notable example being the flintlock musket. You see, back then if ... » read more

New Architectures, Much Faster Chips


The chip industry is making progress in multiple physical dimensions and with multiple architectural approaches, setting the stage for huge performance increases based on more modular and heterogeneous designs, new advanced packaging options, and continued scaling of digital logic for at least a couple more process nodes. A number of these changes have been discussed in recent conferences. I... » read more

Rethinking Architectures Based On Power


The newest chips being developed for everything from the cloud to the edge of the network look nothing like designs of even a year or two ago. They are architected for speed, from the throughput of high-speed buses and external interconnects to the customized accelerators and arrays of redundant MACs. But many of these designs have barely scratched the surface for saving power, which will becom... » read more

Challenges For Compute-In-Memory Accelerators


A compute-in-memory (CIM) accelerator does not simply replace conventional logic. It's a lot more complicated than that. Regardless of the memory technology, the accelerator redefines the latency and energy consumption characteristics of the system as a whole. When the accelerator is built from noisy, low-precision computational elements, the situation becomes even more complex. Tzu-Hsian... » 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

Using Machine Learning To Break Down Silos


Jeff David, vice president of AI solutions at PDF Solutions, talks with Semiconductor Engineering about where machine learning can be applied into semiconductor manufacturing, how it can be used to break down silos around different process steps, how active learning works with human input to tune algorithms, and why it’s important to be able to choose different different algorithms for differ... » read more

Where Is The Edge?


Mike Fitton, senior director of strategic planning at Achronix, talks about what the edge will look like, how that fits in with the cloud, what the requirements are both for processing and for storage, and how this concept will evolve.   Edge Knowledge Center Top stories, videos, blogs, white papers all related to the Edge » read more

Memory Subsystems In Edge Inferencing Chips


Geoff Tate, CEO of Flex Logix, talks about key issues in a memory subsystem in an inferencing chip, how factors like heat can affect performance, and where these kinds of chips will be used. » read more

How To Improve ML Power/Performance


Raymond Nijssen, vice president and chief technologist at Achronix, talks about the shift from brute-force performance to more power efficiency in machine learning processing, the new focus on enough memory bandwidth to keep MAC functions busy, and how dynamic range, precision and locality can be modified to improve speed and reduce power. » read more

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