@inproceedings{c9623e41bb924c63a5c372da8385bbd5,
title = "Boosting the Accuracy of SRAM-Based in-Memory Architectures Via Maximum Likelihood-Based Error Compensation Method",
abstract = "SRAM-based analog in-memory computing (IMC) architectures have demonstrated high energy efficiency and compute density over digital accelerators for machine learning. However, their compute SNR and achievable dot product (DP) dimension are limited by the analog nature of computations. We present a Maximum Likelihood (ML)-based statistical Error Compensation (MLEC) method to enhance the accuracy of binary DPs in a 6T SRAM-based IMC. MLEC leverages the IMC architecture to extract multiple observations and implements an approximate ML detection rule. Employing simulations in a 28nm CMOS and behavioral modeling, we show that MLEC enhances the compute SNR by 5dB-to-30dB over a conventional IMC with an energy overhead ranging from 10%-to-30% for DP dimensions of 64-to-256.",
keywords = "in-memory computing, maximum likelihood detection, statistical error compensation",
author = "Hyungyo Kim and Naresh Shanbhag",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/ICASSP49357.2023.10095785",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
address = "United States",
}