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|Title:||Recovering accuracy of RRAM-based CIM for binarized neural network via Chip-in-the-loop training||Authors:||Chong, Yi Sheng
Goh, Wang Ling
Ong, Yew Soon
Nambiar, Vishnu P.
Do, Anh Tuan
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2022||Source:||Chong, Y. S., Goh, W. L., Ong, Y. S., Nambiar, V. P. & Do, A. T. (2022). Recovering accuracy of RRAM-based CIM for binarized neural network via Chip-in-the-loop training. 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 2958-2962. https://dx.doi.org/10.1109/ISCAS48785.2022.9937271||Project:||A1687b0033||Abstract:||Resistive random access memory (RRAM) based computing-in-memory (CIM) is attractive for edge artificial intelligence (AI) applications, thanks to its excellent energy efficiency, compactness and high parallelism in matrix vector multiplication (MatVec) operations. However, existing RRAM-based CIM designs often require complex programming scheme to precisely control the RRAM cells to reach the desired resistance states so that the neural network classification accuracy is maintained. This leads to large area and energy overhead as well as low RRAM area utilization. Hence, compact RRAMbased CIM with simple pulse-based programming scheme is thus more desirable. To achieve this, we propose a chip-in-the-loop training approach to compensate for the network performance drop due to the stochastic behavior of the RRAM cells. Note that, although the target RRAM cell here is a two-state RRAM (i.e binary, having only high and low resistance states), their inherent analog resistance values are used in the CIM operation. Our experiment using a 4-layer fully-connected binary neural network (BNN) showed that after retraining, the RRAM-based network accuracy can be recovered, regardless of the RRAM resistance distribution and RHRS/RLRS resistance ratio.||URI:||https://hdl.handle.net/10356/159308||DOI:||10.1109/ISCAS48785.2022.9937271||Rights:||© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ISCAS48785.2022.9937271.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
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Updated on Dec 7, 2022
Updated on Dec 7, 2022
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