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 ...
There could be a new era of codesign dawning for machine learning, one that moves away from the training and inference separations and toward far less dense networks with highly sparse weights and ...
A team of researchers in Japan released Fugaku-LLM, a large language model with enhanced Japanese language capability, using the RIKEN supercomputer Fugaku. A team of researchers in Japan released ...
In this video, Jakub Kurzak, Research Assistant Professor at the University of Tennessee’s Innovative Computing Laboratory, discusses the Software for Linear Algebra Targeting Exascale (SLATE) project ...
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 ...
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