论文标题
基于自旋的神经形态电路的电触发随机性：自我调整到变化
Electrically-Tunable Stochasticity for Spin-based Neuromorphic Circuits: Self-Adjusting to Variation
论文作者
论文摘要
解决节能方法用于利用神经形态体系结构内的低能屏障纳米磁性设备。使用磁磁性的随机访问记忆（MRAM）概率装置（P-BIT）作为深信仰网络（DBN）中神经元结构的基础，评估并优化了减少磁性隧道连接（MTJ）能量屏障的影响，以评估并优化了学习系统中产生的随机性。这可以减轻随机DBN的过程变化敏感性，当能量屏障超过接近零kt时遇到急剧下降。如对MNIST数据集的评估，以0.5 kt的增量为零kt至2.0 kt的能垒数据集，这表明稳定性因子变化了5个数量级。本文开发的自加密电路提供了一种紧凑，低复杂的方法，可减轻过程变化对实际实施和制造的影响。
Energy-efficient methods are addressed for leveraging low energy barrier nanomagnetic devices within neuromorphic architectures. Using a Magnetoresistive Random Access Memory (MRAM) probabilistic device (p-bit) as the basis of neuronal structures in Deep Belief Networks (DBNs), the impact of reducing the Magnetic Tunnel Junction's (MTJ's) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. This can mitigate the process variation sensitivity of stochastic DBNs which encounter a sharp drop-off when energy barriers exceed near-zero kT. As evaluated for the MNIST dataset for energy barriers at near-zero kT to 2.0 kT in increments of 0.5 kT, it is shown that the stability factor changes by 5 orders of magnitude. The self-compensating circuit developed herein provides a compact, and low complexity approach to mitigating process variation impacts towards practical implementation and fabrication.
