Fundamental Limits on the Computational Accuracy of Resistive Crossbar-based In-memory Architectures
Saion K. Roy, Ameya Patil, and Naresh R. Shanbhag · Pages 384–388 · DOI 10.1109/ISCAS48785.2022.9937336
IEEE ISCAS 2022
The publication derives the compute-SNR limits, verifies the behavioral route with circuit simulation, and connects the selected circuit parameters to mapped neural-network accuracy.
Saion K. Roy, Ameya Patil, and Naresh R. Shanbhag · Pages 384–388 · DOI 10.1109/ISCAS48785.2022.9937336
Summary
The paper derives the limits on compute SNR for MRAM-, ReRAM-, and FeFET-based crossbars. SNRmax occurs at the Rs* that balances ADC clipping and quantization, contrast helps only up to 12 to 15, and the SNR-optimal parameters also maximize ResNet-20 accuracy.
Citation
The repository also includes a CITATION.cff file.
S. K. Roy, A. Patil, and N. R. Shanbhag, “Fundamental Limits on the Computational Accuracy of Resistive Crossbar-based In-memory Architectures,” in 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 2022, pp. 384–388, doi: 10.1109/ISCAS48785.2022.9937336.
@inproceedings{roy2022crossbarlimits,
author = {Saion K. Roy and Ameya Patil and Naresh R. Shanbhag},
title = {Fundamental Limits on the Computational Accuracy of Resistive Crossbar-based In-memory Architectures},
booktitle = {2022 IEEE International Symposium on Circuits and Systems (ISCAS)},
pages = {384--388},
year = {2022},
doi = {10.1109/ISCAS48785.2022.9937336}
}Companion repository
The repository turns the original scripts into a reproducible behavioral model and serves this website.
data/network/paper_points.csv.