Spintronic neural systems

Kaushik Roy, Cheng Wang, Sourjya Roy, Anand Raghunathan, Kezhou Yang, Abhronil Sengupta
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Abstract

Neural computing, guided by brain-inspired computational frameworks, promises to realize various cognitive and perception-related tasks. Complementary metal–oxide–semiconductor-based computing machines use orders-of-magnitude more computational resources than the brain on cognitive tasks that humans efficiently perform every day. As a result, we are witnessing a seismic shift in the field of computation. Research efforts are being directed to develop artificial intelligence (AI) hardware that mimics the human brain from a bottom-up perspective — through devices that are more naturally suited to neural computation — and thereby improves the efficiency of performing cognitive tasks. In the attempt to bridge the gap between neuroscience and electronics, here we report on developments in the field of spintronic devices for AI hardware. The dynamics of spintronic devices that can be used for the realization of neural and synaptic functionalities are discussed. A cross-layer perspective extending from the device to the circuit and system levels as a pathway towards efficient neural computing systems is also presented. Spintronic devices for artificial intelligence hardware can bridge the gap between neuroscience and electronics. Here we discuss the dynamics of such devices, enabling neural and synaptic functionalities, alongside a cross-layer approach — from devices to circuits and systems — for efficient neural computing systems.

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自旋电子神经系统
在大脑启发计算框架的指导下,神经计算有望实现各种认知和感知相关任务。与大脑相比,基于互补金属氧化物半导体的计算机器在人类每天高效执行的认知任务中使用的计算资源要多出几个数量级。因此,我们正在目睹计算领域发生的巨大变化。目前的研究方向是开发人工智能(AI)硬件,从自下而上的角度--通过更适合神经计算的设备--模仿人脑,从而提高执行认知任务的效率。为了缩小神经科学与电子学之间的差距,我们在此报告人工智能硬件自旋电子器件领域的发展情况。我们讨论了可用于实现神经和突触功能的自旋电子器件的动态。此外,还介绍了从器件到电路和系统层面的跨层视角,以此作为实现高效神经计算系统的途径。用于人工智能硬件的自旋电子器件可以弥补神经科学与电子学之间的差距。在此,我们讨论了此类器件的动态特性,以及实现神经和突触功能的跨层方法--从器件到电路和系统--以实现高效的神经计算系统。
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