Scaling Deep Learning on Multiple In-Memory Processors

Abstract

Deep learning methods are proven to be state-of-theart in addressing many challenges in machine learning domains. However, it comes at the cost of high computational requirements and energy consumption. The emergence of Processing In Memory (PIM) with diestacking technology presents an opportunity to speed up deep learning computation and reduce energy… (More)

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