Fundamental Limits on the Precision of In-memory Architectures

Sujan K. Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper obtains the fundamental limits on the computational precision of in-memory computing architectures (IMCs). Various compute SNR metrics for IMCs are defined and their interrelationships analyzed to show that the accuracy of IMCs is fundamentally limited by the compute SNR (SNRa) of its analog core, and that activation, weight and output precision needs to be assigned appropriately for the final output SNR SNRT ? SNRa. The minimum precision criterion (MPC) is proposed to minimize the output and hence the column analog-to-digital converter (ADC) precision. The charge summing (QS) compute model and its associated IMC QS-Arch are studied to obtain analytical models for its compute SNR, minimum ADC precision, energy and latency. Compute SNR models of QS-Arch are validated via Monte Carlo simulations in a 65 nm CMOS process. Employing these models, upper bounds on SNRa of a QS-Arch-based IMC employing a 512 row SRAM array are obtained and it is shown that QS-Arch's energy cost reduces by 3.3× for every 6 dB drop in SNRa, and that the maximum achievable SNRa reduces with technology scaling while the energy cost at the same SNRa increases. These models also indicate the existence of an upper bound on the dot product dimension N due to voltage headroom clipping, and this bound can be doubled for every 3 dB drop in SNRa.

Original languageEnglish (US)
Article number9256802
JournalIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2020-November
DOIs
StatePublished - Nov 2 2020
Event39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States
Duration: Nov 2 2020Nov 5 2020

Keywords

  • accelerator
  • compute in-memory
  • in-memory accuracy
  • in-memory computing
  • in-memory noise
  • in-memory precision
  • machine learning
  • taxonomy of in-memory

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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