A Non-conventional Sum-and-Max based Neural Network layer for Low Power Classification

Luciano Pronoz, Mauro Mangia, Fabio Pareschizy, Riccardo Rovatti, Gianluca Settizy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The increasing need for small and low-power Deep Neural Networks (DNNs) for edge computing applications involves the investigation of new architectures that allow good performance on low-resources/mobile devices. To this aim, many different structures have been proposed in the literature, mainly targeting the reduction in the costs introduced by the Multiply and Accumulate (MAC) primitive. In this work, a DNN layer based on the novel Sum and Max (SAM) paradigm is proposed. It does not require either the use of multiplications or the insertion of complex non-linear operations. Furthermore, it is especially prone to aggressive pruning, thus needing a very low number of parameters to work. The layer is tested on a simple classification task and its cost is compared with a classic DNN layer with equivalent accuracy based on the MAC primitive, in order to assess the reduction of resources that the use of this new structure could introduce.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Circuits and Systems, ISCAS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages712-716
Number of pages5
ISBN (Electronic)9781665484855
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States
Duration: May 27 2022Jun 1 2022

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2022-May
ISSN (Print)0271-4310

Conference

Conference2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Country/TerritoryUnited States
CityAustin
Period05/27/2206/1/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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