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Compute SNDR-Boosted 22-nm MRAM-Based In-Memory Computing Macro Using Statistical Error Compensation

  • Saion K. Roy
  • , Han Mo Ou
  • , Mostafa G. Ahmed
  • , Peter Deaville
  • , Bonan Zhang
  • , Naveen Verma
  • , Pavan K. Hanumolu
  • , Naresh R. Shanbhag

Research output: Contribution to journalArticlepeer-review

Abstract

The accuracy of embedded non-volatile memory (eNVM) in-memory computing (IMC) designs is primarily limited by analog non-idealities. This article introduces a magnetoresistive random-access memory (MRAM) IMC macro in 22-nm featuring offset-compensating current sensing (OCCS) to reduce the static analog-to-digital converter (ADC) column mismatch and a low-overhead statistical error compensation (SEC) block compensating for non-linearity arising due to bitline/source-line (BL/SL) wire parasitics. Both assist in enhancing the bank-level compute signal-to-noise-plus-distortion ratio (SNDR). As the inner dimension of the matrix-vector multiplication (MVM) increases, the compute SNDR reduces due to increased location-dependent non-linearity arising from BL/SL wire parasitics. An SEC-enabled SNDR boost of 2.7-6 dB is obtained over different operating points. This boost can be balanced to achieve a substantial 5× reduction in energy per 1-b operation while incurring a modest SEC energy overhead of 0.8% and area overhead of 12.2%. Finally, the study demonstrates an SEC-enabled increase in neural network (NN) accuracy from 74.8% to 82.0% for CIFAR-10 by last layer mapping of ResNet-20, without resorting to noise-aware training.

Original languageEnglish (US)
Pages (from-to)1092-1102
Number of pages11
JournalIEEE Journal of Solid-State Circuits
Volume60
Issue number3
DOIs
StatePublished - 2025

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • Compute signal-to-noise-plus-distortion ratio (SNDR)
  • embedded non-volatile memory (eNVM)
  • in-memory computing (IMC)
  • magnetoresistive random-access memory (MRAM)

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