基于新兴技术的神经形态系统编程框架

Catherine D. Schuman, J. Plank, G. Rose, Gangotree Chakma, A. Wyer, Grant Bruer, N. Laanait
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引用次数: 9

摘要

神经形态计算是后摩尔定律时代一项很有前途的技术。近年来出现了各种各样的神经形态计算机(NC)体系结构,从传统的全数字CMOS到具有新颖的、超越CMOS组件的纳米级实现。由于nc的架构与冯·诺伊曼的架构有很大的不同,我们将如何编程和使用nc已经有了一些主要的问题。当与使用新兴设备技术的实现相结合时,这增加了与编程设备相关的额外问题,很明显,我们必须定义一种新的方法来编程和开发NC设备。在这项工作中,我们讨论了用新兴技术实现的数控设备的编程框架。我们讨论了我们如何应用这个框架来编程一个用金属氧化物忆阻器实现的数控系统。我们利用该框架为记忆数控装置开发了两个应用程序:一个简单的多路复用器和一个简单的控制任务(车杆问题)。最后,我们讨论了如何将该框架扩展到使用各种新型器件组件和材料实现的NC系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A programming framework for neuromorphic systems with emerging technologies
Neuromorphic computing is a promising post-Moore's law era technology. A wide variety of neuromorphic computer (NC) architectures have emerged in recent years, ranging from traditional fully digital CMOS to nanoscale implementations with novel, beyond CMOS components. There are already major questions associated with how we are going to program and use NCs simply because of how radically different their architecture is as compared with the von Neumann architecture. When coupled with the implementations using emerging device technologies, which add additional issues associated with programming devices, it is clear that we must define a new way to program and develop for NC devices. In this work, we discuss a programming framework for NC devices implemented with emerging technologies. We discuss how we have applied this framework to program a NC system implemented with metal oxide memristors. We utilize the framework to develop two applications for the memristive NC device: a simple multiplexer and a simple control task (the cart-pole problem). Finally, we discuss how this framework can be extended to NC systems implemented with a variety of novel device components and materials.
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