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Evaluating Theoretical Baselines for ML Benchmarking Across Different Accelerators | IEEE Journals & Magazine | IEEE Xplore

Evaluating Theoretical Baselines for ML Benchmarking Across Different Accelerators


Abstract:

This article provides a theoretical baseline for enabling performance predictions across a broad spectrum of machine learning hardware architecture designs while consider...Show More

Abstract:

This article provides a theoretical baseline for enabling performance predictions across a broad spectrum of machine learning hardware architecture designs while considering the efficiency of optimizations.
Published in: IEEE Design & Test ( Volume: 39, Issue: 3, June 2022)
Page(s): 28 - 36
Date of Publication: 03 March 2021

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