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

2019/12/18 by Tien-Ju Yang, Vivienne Sze, Yang, Tien-Ju +1 · 2 citations
Computer Science · Engineering · #Advanced Memory and Neural Computing #Advanced Neural Network Applications #CCD and CMOS Imaging Sensors #Computer Vision and Pattern Recognition (cs.CV) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #cs.CV #cs.ET

paper · pdf · doi:10.48550/arxiv.1912.12167

Accepted by IEDM 2019

arxiv created 2019/12/18 · openalex publication_date 2019/12/18 · arxiv updated 2019/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

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.

Citations

• Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices
• Efficient Processing of Deep Neural Networks: A Tutorial and Survey
• Neuro-Inspired Computing With Emerging Nonvolatile Memorys

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• Forward Target Propagation: A Forward-Only Approach to Global Error Credit Assignment via Local Losses

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