Behavioral Level Simulation Framework to Support Error-Aware CNN Training with In-Memory Computing

Shih Han Chang, Chien Nan Jimmy Liu, Alexandra Kuster

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

4 Scopus citations

Abstract

In recent years, in-memory computing (IMC) is a promising technique to solve the bottleneck of data movement in edge AI devices. To perform some simple computation in memory, the IMC designs often adopt analog operations, which may incur inevitable computation errors. To recover the accuracy loss, adding some random disturbance in CNN training is a straightforward approach to make it more tolerant to computation errors. However, random values may be quite different to the real run-time errors caused by non-ideal effects. In this work, we propose a hierarchical simulation framework for the IMC systems to support error-aware CNN training. This framework includes an efficient approach to build accurate IMC behavioral models that reflect real non-ideal effects. By using the behavioral model, an accurate high-level error model can be built efficiently to provide run-time errors for CNN training and error rate verification. As shown in the simulation results, the error-aware CNN training with the proposed models efficiently improves the CNN accuracy in real applications with almost no accuracy loss.

Original languageEnglish
Title of host publicationProceedings - 2022 18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665467032
DOIs
StatePublished - 2022
Event18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2022 - Villasimius, Italy
Duration: 12 Jun 202215 Jun 2022

Publication series

NameProceedings - 2022 18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2022

Conference

Conference18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2022
Country/TerritoryItaly
CityVillasimius
Period12/06/2215/06/22

Keywords

  • Analog CAD
  • behavioral model
  • in-memory computing

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