Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators

12/18/2019

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by   Tien-Ju Yang, et al.

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This paper describes various design considerations for deep neural networks that enable them to operate efficiently and accurately on processing-in-memory accelerators. We highlight important properties of these accelerators and the resulting design considerations using experiments conducted on various state-of-the-art deep neural networks with the large-scale ImageNet dataset.