Robustness of Binary Stochastic Neurons Implemented With Low Barrier Nanomagnets Made of Dilute Magnetic Semiconductors
Robustness of Binary Stochastic Neurons Implemented With Low Barrier Nanomagnets Made of Dilute Magnetic Semiconductors
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用稀磁半导体制成的低势垒纳米磁体实现二元随机神经元的鲁棒性
DOI：
10.1109/lmag.2022.3202135
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发表时间：
2022
影响因子：
1.2
通讯作者：
Bandyopadhyay, Supriyo
中科院分区：
文献类型：
--
作者：
Rahman, Rahnuma;Bandyopadhyay, Supriyo
二进制随机神经元（BSN）是机器学习的优秀硬件加速器。用于实现它们的流行平台是具有平面内磁各向异性的低能或零能垒纳米磁体（例如，具有非常小偏心率的圆盘或准椭圆圆盘）。不幸的是，如果纳米磁体由具有大的饱和磁化强度的普通金属铁磁体（Co、Ni、Fe）制成，则这种纳米磁体的横向形状的小的几何变化可以产生BSN响应时间的大的变化。此外，响应时间对初始条件变得非常敏感，即，初始磁化方向。在这封信中，我们表明，如果纳米磁体是由饱和磁化强度比普通金属铁磁体小得多的稀磁半导体制成的，那么它们的响应时间的变化（由于形状变化和初始条件的变化）将被大大抑制。这大大减少了设备间的变化，这是一个严重的挑战，大规模的神经形态系统。因此，一个简单的材料选择可以缓解纳米磁体概率计算中最严重的问题之一。
Binary stochastic neurons (BSNs) are excellent hardware accelerators for machine learning. A popular platform for implementing them is 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. In addition, the response times become very sensitive to initial conditions, i.e., the initial magnetization orientation. In this letter, we show that if the nanomagnets are made of dilute magnetic semiconductors with much smaller saturation magnetization than common metallic ferromagnets, 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. A simple material choice can, therefore, alleviate one of the most aggravating problems in probabilistic computing with nanomagnets.
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影响因子：
1.2
作者：
Faria, Rafatul;Camsari, Kerem Yunus;Datta, Supriyo
通讯作者：
Datta, Supriyo
影响因子：
1.2
作者：
Hassan, Orchi;Faria, Rafatul;Datta, Supriyo
通讯作者：
Datta, Supriyo
用于固定权重神经网络的纯电容突触矩阵
DOI：
10.1109/31.68299
发表时间：
1991
期刊：
IEEE Transactions on Circuits and Systems
影响因子：
--
作者：
U. Çilingiroğlu
通讯作者：
U. Çilingiroğlu
影响因子：
1.2
作者：
Abeed, Md. Ahsanul;Bandyopadhyay, Supriyo
通讯作者：
Bandyopadhyay, Supriyo
影响因子：
0.9
作者：
J. Wegrowe;M. Ciornei
通讯作者：
M. Ciornei