Printed machine learning classifiers

Muhammad Husnain Mubarik, Dennis D. Weller, Nathaniel Bleier, Matthew Tomei, Jasmin Aghassi-Hagmann, Mehdi B. Tahoori, Rakesh Kumar

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

Abstract

A large number of application domains have requirements on cost, conformity, and non-toxicity that silicon-based computing systems cannot meet, but that may be met by printed electronics. For several of these domains, a typical computational task to be performed is classification. In this work, we explore the hardware cost of inference engines for popular classification algorithms (Multi-Layer Perceptrons, Support Vector Machines (SVMs), Logistic Regression, Random Forests and Binary Decision Trees) in EGT and CNT-TFT printed technologies and determine that Decision Trees and SVMs provide a good balance between accuracy and cost. We evaluate conventional Decision Tree and SVM architectures in these technologies and conclude that their area and power overhead must be reduced. We explore, through SPICE and gate-level hardware simulations and multiple working prototypes, several classifier architectures that exploit the unique cost and implementation tradeoffs in printed technologies - a) Bespoke printed classifers that are customized to a model generated for a given application using specific training datasets, b) Lookup-based printed classifiers where key hardware computations are replaced by lookup tables, and c) Analog printed classifiers where some classifier components are replaced by their analog equivalents. Our evaluations show that bespoke implementation of EGT printed Decision Trees has 48.9× lower area (average) and 75.6× lower power (average) than their conventional equivalents; corresponding benefits for bespoke SVMs are 12.8× and Decision outperform 12.7× respectively. Lookup-based Trees their non-lookup bespoke equivalents by 38% and 70%; lookup-based SVMs are better by 8% and 0.6%. Analog printed Decision Trees provide 437× area and 27× power benefits over digital bespoke counterparts; analog SVMs yield 490× area and 12× power improvements. Our results and prototypes demonstrate feasibility of fabricating and deploying battery and self-powered printed classifiers in the application domains of interest.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2020
PublisherIEEE Computer Society
Pages73-87
Number of pages15
ISBN (Electronic)9781728173832
DOIs
StatePublished - Oct 2020
Event53rd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2020 - Virtual, Athens, Greece
Duration: Oct 17 2020Oct 21 2020

Publication series

NameProceedings of the Annual International Symposium on Microarchitecture, MICRO
Volume2020-October
ISSN (Print)1072-4451

Conference

Conference53rd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2020
CountryGreece
CityVirtual, Athens
Period10/17/2010/21/20

Keywords

  • Machine learning
  • Printed electronics

ASJC Scopus subject areas

  • Hardware and Architecture

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