An energy-efficient VLSI architecture for pattern recognition via deep embedding of computation in SRAM

Mingu Kong, Min Sun Keel, Naresh R Shanbhag, Sean Eilert, Ken Curewitz

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

Abstract

In this paper, we propose the concept of compute memory, where computation is deeply embedded into the memory (SRAM). This deep embedding enables multi-row read access and analog signal processing. Compute memory exploits the relaxed precision and linearity requirements of pattern recognition applications. System-level simulations incorporating various deterministic errors from analog signal chain demonstrates the limited accuracy of analog processing does not significantly degrade the system performance, which means the probability of pattern detection is minimally impacted. The estimated energy saving is 63 % as compared to the conventional system with standard embedded memory and parallel processing architecture, for 256×256 target image.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8326-8330
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Country/TerritoryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • Analog processing
  • Associative memory
  • Compute memory
  • Machine learning
  • Pattern recognition

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'An energy-efficient VLSI architecture for pattern recognition via deep embedding of computation in SRAM'. Together they form a unique fingerprint.

Cite this