@inproceedings{bf1e666a6daf4e7f97798b4d860190bb,
title = "Fundamental Limits on the Computational Accuracy of Resistive Crossbar-based In-memory Architectures",
abstract = "In-memory computing (IMC) architectures exhibit an intrinsic trade-off between computational accuracy and energy efficiency. This paper determines the fundamental limits on the compute SNR of MRAM-, ReRAM-, and FeFET-based crossbars by employing statistical signal and noise models. For a specific dot-product dimension N, the maximum compute SNR (SNRmax) is shown to occur at an optimum value of sensing resistance R-{s} {*} where clipping and quantization noise contributions from the analog-to-digital converter (ADC) are balanced out. SNRmax can be further improved by choosing devices with higher resistive contrast Roff/Ron, e.g., FeFET, but only until it attains a value in the range 12-15. Beyond this point, mismatch in the input digital-to-analog converters (DACs) and bitcell variations begin to dominate the compute SNR. Finally, by mapping a ResNet20 (CIFAR-10) network onto resistive crossbars, it is shown that the array-level compute SNR maximizing circuit parameters also maximizes the network-level accuracy.",
keywords = "FeFET, MRAM, ReRAM, SNR, crossbar, eNVM, in-memory computing",
author = "Roy, {Saion K.} and Ameya Patil and Shanbhag, {Naresh R.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 ; Conference date: 27-05-2022 Through 01-06-2022",
year = "2022",
doi = "10.1109/ISCAS48785.2022.9937336",
language = "English (US)",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "384--388",
booktitle = "IEEE International Symposium on Circuits and Systems, ISCAS 2022",
address = "United States",
}