论文标题
用稀磁性半导体制成的低屏障纳米磁体实现的二元随机神经元的鲁棒性
Robustness of binary stochastic neurons implemented with low barrier nanomagnets made of dilute magnetic semiconductors
论文作者
论文摘要
二进制随机神经元（BSN）是机器学习的出色硬件加速器。实现它们的流行平台是具有平面磁各向异性的低能或零势屏障纳米磁体（例如，圆盘或偏心率很小的圆盘或准纤维盘）。不幸的是，如果纳米磁体由较大的饱和度磁力化的常见金属金属磁铁（CO，Ni，Fe）制成，则此类纳米磁体的侧面形状的微小几何变化会在BSN响应时间中产生巨大变化。另外，响应时间也对初始条件非常敏感。在这里，我们表明，如果纳米磁铁由稀有饱和度磁化的稀磁半导体制成，那么它们的响应时间（由于初始条件下的形状变化和变化）的变异性会大大抑制。这大大减少了设备对设备的变化，这对于大型神经形态系统来说是一个严重的挑战。
Binary stochastic neurons (BSNs) are excellent hardware accelerators for machine learning. A popular platform for implementing them are low- or zero-energy barrier nanomagnets possessing in-plane magnetic anisotropy (e.g. circular disks or quasi-elliptical disks with very small eccentricity). Unfortunately, small geometric variations in the lateral shapes of such nanomagnets can produce large changes in the BSN response times if the nanomagnets are made of common metallic ferromagnets (Co, Ni, Fe) with large saturation magnetization. Additionally, the response times are also very sensitive to initial conditions. Here, we show that if the nanomagnets are made of dilute magnetic semiconductors with much smaller saturation magnetization, then the variability in their response times (due to shape variations and variation in the initial condition) is drastically suppressed. This significantly reduces the device-to-device variation, which is a serious challenge for large scale neuromorphic systems.
