High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...
AI training time is at a point in an exponential where more throughput isn't going to advance functionality much at all. The underlying problem, problem solving by training, is computationally ...
Current custom AI hardware devices are built around super-efficient, high performance matrix multiplication. This category of accelerators includes the host of AI chip startups and defines what more ...
New lower values for p get discovered all the time (maybe once a year). It is conjectured that they will approach 2.0 without ever getting quite to it. Somehow Quanta Mag heard about the new result ...
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