基于随机磁隧道结的硬件感知$in \ situ$ Boltzmann机器学习
Hardware-aware $in \ situ$ Boltzmann machine learning using stochastic magnetic tunnel junctions
摘要：
当前电子产品面临的最大挑战之一是设计和实现硬件神经网络，以执行快速和节能的机器学习。自旋电子学是一个有前途的催化剂，为这一领域的能力，纳秒操作和兼容现有的微电子学。然而，考虑到大规模、可行的神经形态系统，设备属性的可变性是一个严重的问题。在本文中，我们展示了一个自主运行的电路，该电路利用随机磁性隧道结构建的概率神经元执行硬件感知机器学习。我们表明，$in \ situ$学习玻尔兹曼机中的权重和偏置可以对抗设备到设备的变化，并学习有意义的操作（如全加器）的概率分布。这种使用基于自旋电子学的神经元的可扩展自主操作学习电路对于能够在边缘快速有效学习的独立人工智能设备来说可能特别感兴趣。
英文摘要：
One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities of nanosecond operation and compatibility with existing microelectronics. Considering large-scale, viable neuromorphic systems however, variability of device properties is a serious concern. In this paper, we show an autonomously operating circuit that performs hardware-aware machine learning utilizing probabilistic neurons built with stochastic magnetic tunnel junctions. We show that $in \ situ$ learning of weights and biases in a Boltzmann machine can counter device-to-device variations and learn the probability distribution of meaningful operations such as a full adder. This scalable autonomously operating learning circuit using spintronics-based neurons could be especially of interest for standalone artificial-intelligence devices capable of fast and efficient learning at the edge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
