Parapim: A parallel processing-in-memory accelerator for binary-weight deep neural networks

Shaahin Angizi, Zhezhi He, Deliang Fan

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

4 Scopus citations

Abstract

Recent algorithmic progression has brought competitive classification accuracy despite constraining neural networks to binary weights (+1/-1). These findings show remarkable optimization opportunities to eliminate the need for computationally-intensive multiplications, reducing memory access and storage. In this paper, we present ParaPIM architecture, which transforms current Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) sub-arrays to massively parallel computational units capable of running inferences for Binary-Weight Deep Neural Networks (BWNNs). ParaPIM's in-situ computing architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers, accelerate BWNNs inference, eliminate unnecessary off-chip accesses and provide ultra-high internal bandwidth. The device-to-architecture co-simulation results indicate ∼4× higher energy efficiency and 7.3× speedup over recent processing-in-DRAM acceleration, or roughly 5× higher energy-efficiency and 20.5× speedup over recent ASIC approaches, while maintaining inference accuracy comparable to baseline designs.

Original languageEnglish (US)
Title of host publicationASP-DAC 2019 - 24th Asia and South Pacific Design Automation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages127-132
Number of pages6
ISBN (Electronic)9781450360074
DOIs
StatePublished - Jan 21 2019
Externally publishedYes
Event24th Asia and South Pacific Design Automation Conference, ASPDAC 2019 - Tokyo, Japan
Duration: Jan 21 2019Jan 24 2019

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Other

Other24th Asia and South Pacific Design Automation Conference, ASPDAC 2019
CountryJapan
CityTokyo
Period1/21/191/24/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Parapim: A parallel processing-in-memory accelerator for binary-weight deep neural networks'. Together they form a unique fingerprint.

Cite this