By ASU News
August 28, 2023
Researchers at Arizona State University (ASU) and the University of California, Los Angeles hope to enable scientists and processor designers to understand the underlying reasoning of deep learning accelerator designs through explainable-design space exploration (DSE).
ASU’s Shail Dave said hardware and software designs are typically optimized via black box mechanisms that “require excessive amounts of trial runs because of their lack of explainability and reasoning involved in how selecting a design configuration affects the design’s overall quality.”
Explainable-DSE simplifies the accelerator’s decision-making process so choices of design methods can be made in minutes rather than days or weeks, supporting smaller, more systematic, and more energy-efficient models.
Dave’s algorithm can investigate design solutions relating to multiple applications, including those differing in functionality or processing traits, while resolving their product execution inefficiencies.
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