磁隧道结实现的概率深脉冲神经系统
Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction
摘要：
深度脉冲神经网络正成为认知计算平台越来越强大的工具。然而，大多数现有的文献，这种计算模型的开发与底层硬件实现的有限的见解，导致面积和功耗昂贵的设计。虽然最近已经提出了几种模拟神经操作的神经模拟设备，但它们的功能仅限于非常简单的神经模型，这可能证明在复杂的识别任务中效率低下。在这项工作中，我们冒险进入相对未开发的领域，利用固有的设备随机性的神经模拟设备在时域中的概率框架中模拟复杂的神经功能。我们考虑实现一个深脉冲神经网络能够执行高精度和低延迟的分类任务，其中的神经计算单元是由磁隧道结的随机切换行为。仿真研究表明，在$45nm$技术中，$20\times$的能量改善超过基线CMOS设计。
英文摘要：
Deep Spiking Neural Networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing literature on such computing models are developed with limited insights on the underlying hardware implementation, resulting in area and power expensive designs. Although several neuromimetic devices emulating neural operations have been proposed recently, their functionality has been limited to very simple neural models that may prove to be inefficient at complex recognition tasks. In this work, we venture into the relatively unexplored area of utilizing the inherent device stochasticity of such neuromimetic devices to model complex neural functionalities in a probabilistic framework in the time domain. We consider the implementation of a Deep Spiking Neural Network capable of performing high accuracy and low latency classification tasks where the neural computing unit is enabled by the stochastic switching behavior of a Magnetic Tunnel Junction. Simulation studies indicate an energy improvement of $20\times$ over a baseline CMOS design in $45nm$ technology.
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