A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system

Yu Gu; Puyang Huang; Tianhao Chen; Chenyi Fu; Aitian Chen; Shouzhong Peng; Xixiang Zhang; Xufeng Kou

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arXiv · 2403.19374

A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system

Yu Gu·Puyang Huang·Tianhao Chen·Chenyi Fu·Aitian Chen·Shouzhong Peng·Xixiang Zhang·Xufeng Kou

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Abstract

We report a spin-orbit torque(SOT) magnetoresistive random-access memory(MRAM)-based probabilistic binary neural network(PBNN) for resource-saving and hardware noise-tolerant computing applications. With the presence of thermal fluctuation, the non-destructive SOT-driven magnetization switching characteristics lead to a random weight matrix with controllable probability distribution. In the meanwhile, the proposed CIM architecture allows for the concurrent execution of the probabilistic vector-matrix multiplication (PVMM) and binarization. Furthermore, leveraging the effectiveness of random binary cells to propagate multi-bit probabilistic information, our SOT-MRAM-based PBNN system achieves a 97.78\% classification accuracy under a 7.01\% weight variation on the MNIST database through 10 sampling cycles, and the number of bit-level computation operations is reduced by a factor of 6.9 compared to that of the full-precision LeNet-5 network. Our work provides a compelling framework for the design of reliable neural networks tailored to the applications with low power consumption and limited computational resources.

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Yu Gu, Puyang Huang, Tianhao Chen, Chenyi Fu, Aitian Chen, Shouzhong Peng, Xixiang Zhang, Xufeng Kou. 2024-03-28. A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system. https://arxiv.org/abs/2403.19374

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