تقرير
A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system
| العنوان: | A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system |
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| المؤلفون: | Gu, Yu, Huang, Puyang, Chen, Tianhao, Fu, Chenyi, Chen, Aitian, Peng, Shouzhong, Zhang, Xixiang, Kou, Xufeng |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Emerging Technologies, Electrical Engineering and Systems Science - Systems and Control, 94C60, B.2.4, B.3.0 |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.19374 |
| رقم الأكسشن: | edsarx.2403.19374 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |