On the Netlist Gate-level Pruning for Tree-based Machine Learning Accelerators

Brunno A. De Abreu, Guilherme Paim, Jorge Castro-Godinez, Mateus Grellert, Sergio Bampi

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

2 Scopus citations

Abstract

The technology advances in the recent years have led to the spread use of Machine Learning (ML) models in embedded systems. Due to the battery limitations of such edge devices, energy consumption has become a major problem. Tree-based models, such as Decision Trees (DTs) and Random Forests (RFs), are well-known ML tools that provide higher than standard accuracy results for several tasks. These tools are convenient for battery-powered devices due to their simplicity, and they can be further optimized with approximate computing techniques. This paper explores gate-level pruning for DTs and RFs. By using a framework that generates VLSI descriptions of the ML models, we investigate gate-level pruning to the mapped netlist generated after logic synthesis for three case studies. Several analyses on the energy- and area-accuracy trade-offs were performed and we found that we can obtain significant energy and area savings for small or even negligible accuracy drops, which indicates that pruning techniques can be applied to optimize tree-based hardware implementations.

Original languageEnglish
Title of host publication2022 IEEE 13th Latin American Symposium on Circuits and Systems, LASCAS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665420082
DOIs
StatePublished - 2022
Event13th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2022 - Santiago, Chile
Duration: 1 Mar 20224 Mar 2022

Publication series

Name2022 IEEE 13th Latin American Symposium on Circuits and Systems, LASCAS 2022

Conference

Conference13th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2022
Country/TerritoryChile
CitySantiago
Period1/03/224/03/22

Keywords

  • Approximate Computing
  • Machine Learning
  • Tree-based Models

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