量子化人工神经网络与自旋电子随机计算实现
Saadi Sabyasachi1, Walid Al Misba1, Yixin Shao2
1Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA 23284, United States of America.
Nanotechnology
|June 8, 2025
概括
量子化随机计算 (SC) 使用随机磁道连接 (s-MTJs) 显著降低了人工神经网络 (ANN) 的能耗和延迟,同时保持了高精度. 这种方法优化了资源密集的矩阵向量乘法,以实现高效的硬件实现.
科学领域:
- * 计算机科学 计算机科学
- * 电气工程 电气工程
- * 材料科学 材料科学
背景情况:
- *人工神经网络 (ANN) 推断需要大量的能量和设备资源,因为矩阵向量乘法.
- * 随机计算 (SC) 为ANN提供了一个有希望的,资源密集度较低的替代方案,利用随机数生成器 (RNG).
- * 随机磁道连接 (s-MTJs) 可以为基于硬件的SC生成随机位流,但之前的工作集中在模拟权重上.
研究的目的:
- * 为了研究SC对矩阵向量乘法与定量化突触权重和输出的有效性.
- * 用实验性s-MTJ比特流和离散重量/节点状态来评估量子化SC-ANN的性能.
- * 为了比较量子化SC-ANNs与模拟实现的能源消耗,延迟和准确性.
主要方法:
- * 实现了量子化SC-ANN,用于权重和隐藏层节点的5个和11个离散状态.
- *利用实验获得的不同长度 (100-500位) 的s-MTJ比特流.
- *在MNIST数据集上使用神经网络进行训练和推断,使用一个和三个隐藏层的神经网络.
主要成果:
- *与模拟s-MTJ ANNs相比,量子化SC-ANNs显示了降低的延迟 (9×) 和能源消耗 (2.6×).
- *与SC进行训练,在所有配置中始终提高了准确性.
- *用400位随机位流和三个隐藏层实现了96.82%的峰值精度.
结论:
- *量子化SC-ANN有效降低硬件资源需求,提高能源效率.
- * 在SC-ANNs中使用离散量子化状态可以保持准确性,同时提高性能.
- * 这种方法为使用s-MTJs实现的节能,高性能ANN提供了可行的途径.
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