1. A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system
- Author
-
Gu, Yu, Huang, Puyang, Chen, Tianhao, Fu, Chenyi, Chen, Aitian, Peng, Shouzhong, Zhang, Xixiang, and Kou, Xufeng
- Subjects
Computer Science - Emerging Technologies ,Electrical Engineering and Systems Science - Systems and Control ,94C60 ,B.2.4 ,B.3.0 - 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., Comment: 5 pages, 10 figures
- Published
- 2024