KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting

Hassan Dbouk, Sujan K. Gonugondla, Charbel Sakr, Naresh R. Shanbhag

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

Abstract

This paper presents a 0.34 uJ/decision deep learning-based classifier for keyword spotting (KWS) in 65 nm CMOS with all weights stored on-chip. This work adapts a Recurrent Attention Model (RAM) algorithm for the KWS task, and employs an in-memory computing (IMC) architecture to achieve up to 9× savings in energy/decision and more than 23× savings in EDP of decisions over a state-of-the art IMC IC for KWS using the Google Speech dataset while achieving the highest reported decision throughput of 18.32 k decisions/s.

Original languageEnglish (US)
Title of host publication2020 IEEE Custom Integrated Circuits Conference, CICC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728160313
DOIs
StatePublished - Mar 2020
Externally publishedYes
Event2020 IEEE Custom Integrated Circuits Conference, CICC 2020 - Boston, United States
Duration: Mar 22 2020Mar 25 2020

Publication series

NameProceedings of the Custom Integrated Circuits Conference
Volume2020-March
ISSN (Print)0886-5930

Conference

Conference2020 IEEE Custom Integrated Circuits Conference, CICC 2020
Country/TerritoryUnited States
CityBoston
Period3/22/203/25/20

Keywords

  • in-memory computing
  • keyword spotting
  • machine learning
  • recurrent attention networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting'. Together they form a unique fingerprint.

Cite this