Accelerating Deep Neural Networks in Processing-in-Memory Platforms: Analog or Digital Approach?

Shaahin Angizi, Zhezhi He, Dayane Reis, Xiaobo Sharon Hu, Wilman Tsai, Shy Jay Lin, Deliang Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Nowadays, research topics on AI accelerator designs have attracted great interest, where accelerating Deep Neural Network (DNN) using Processing-in-Memory (PIM) platforms is an actively-explored direction with great potential. PIM platforms, which simultaneously aims to address power-and memory-wall bottlenecks, have shown orders of performance enhancement in comparison to the conventional computing platforms with Von-Neumann architecture. As one direction of accelerating DNN in PIM, resistive memory array (aka. crossbar) has drawn great research interest owing to its analog current-mode weighted summation operation which intrinsically matches the dominant Multiplication-and-Accumulation (MAC) operation in DNN, making it one of the most promising candidates. An alternative direction for PIM-based DNN acceleration is through bulk bit-wise logic operations directly performed on the content in digital memories. Thanks to the high fault-tolerant characteristic of DNN, the latest algorithmic progression successfully quantized DNN parameters to low bit-width representations, while maintaining competitive accuracy levels. Such DNN quantization techniques essentially convert MAC operation to much simpler addition/subtraction or comparison operations, which can be performed by bulk bit-wise logic operations in a highly parallel fashion. In this paper, we build a comprehensive evaluation framework to quantitatively compare and analyze aforementioned PIM based analog and digital approaches for DNN acceleration.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
PublisherIEEE Computer Society
Pages197-202
Number of pages6
ISBN (Electronic)9781538670996
DOIs
StatePublished - Jul 2019
Externally publishedYes
Event18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 - Miami, United States
Duration: Jul 15 2019Jul 17 2019

Publication series

NameProceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume2019-July
ISSN (Print)2159-3469
ISSN (Electronic)2159-3477

Conference

Conference18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
CountryUnited States
CityMiami
Period7/15/197/17/19

Keywords

  • crossbar
  • in-memory bulk logic
  • Neural network acceleration
  • Processing-in-memory

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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