A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system
<|reference_start|>A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system: 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.<|reference_end|>
@article{gu2024a,
    title={A noise-tolerant, resource-saving probabilistic binary neural network
  implemented by the SOT-MRAM compute-in-memory system},
    author={Yu Gu and Puyang Huang and Tianhao Chen and Chenyi Fu and Aitian Chen
  and Shouzhong Peng and Xixiang Zhang and Xufeng Kou},
    journal={arXiv preprint arXiv:2403.19374},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19374},
    primaryClass={cs.ET cs.SY eess.SY}
}