2024/03/28 by Yu Gu, Gu, Yu, Puyang Huang +13
Computer Science · Neuroscience · #94C60 #B.2.4 #B.3.0 #Brain Tumor Detection and Classification #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning and ELM #Neural Networks and Applications #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2403.19374
openalex publication_date 2024/03/28 · openalex created_date 2024/03/30 · openalex updated_date 2026/07/28
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.