Statistical computing framework and demonstration for in-memory computing systems

Bonan Zhang, Peter Deaville, Naveen Verma

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

Abstract

With the increasing importance of data-intensive workloads, such as AI, in-memory computing (IMC) has demonstrated substantial energy/throughput benefits by addressing both compute and data-movement/accessing costs, and holds significant further promise by its ability to leverage emerging forms of highly-scaled memory technologies. However, IMC fundamentally derives its advantages through parallelism, which poses a trade-off with SNR, whereby variations and noise in nanoscaled devices directly limit possible gains. In this work, we propose novel training approaches to improve model tolerance to noise via a contrastive loss function and a progressive training procedure. We further propose a methodology for modeling and calibrating hardware noise, efficiently at the level of a macro operation and through a limited number of hardware measurements. The approaches are demonstrated on a fabricated MRAM-based IMC prototype in 22nm FD-SOI, together with a neural network training framework implemented in PyTorch. For CIFAR-10/100 classifications, model performance is restored to the level of ideal noise-free execution, and generalized performance of the trained model deployed across different chips is demonstrated.

Original languageEnglish (US)
Title of host publicationProceedings of the 59th ACM/IEEE Design Automation Conference, DAC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages979-984
Number of pages6
ISBN (Electronic)9781450391429
DOIs
StatePublished - Jul 10 2022
Event59th ACM/IEEE Design Automation Conference, DAC 2022 - San Francisco, United States
Duration: Jul 10 2022Jul 14 2022

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference59th ACM/IEEE Design Automation Conference, DAC 2022
Country/TerritoryUnited States
CitySan Francisco
Period7/10/227/14/22

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

Keywords

  • MRAM
  • deep learning
  • in-memory computing
  • statistical computing

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