嵌入式系统的仿生超低功耗神经形态计算引擎

Beiye Liu, Miao Hu, Hai Helen Li, Yiran Chen, C. Xue
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引用次数: 3

摘要

神经形态计算受人脑工作机制的启发,是近年来解决计算系统功能有限与应用日益多样化之间矛盾的一个研究热点。在这项工作中,我们将介绍我们对一个名为Centaur的仿生神经形态嵌入式计算引擎的研究,该引擎旨在通过采用仿生计算模型和先进的忆阻器技术实现超过1 teraflops / watt的超高功率效率。Centaur设计的成功可能会将嵌入式系统的功率效率从目前的水平提高三个数量级,而该设计的小占地面积和实时可重新配置性使其易于集成到mpsoc中,从而实现许多新兴的移动和嵌入式应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bio-inspired ultra lower-power neuromorphic computing engine for embedded systems
Neuromorphic computing, which is inspired by the working mechanism of human brain, recently emerges as a hot research area to combat the contradiction between the limited functions of computing systems and the ever increasing variety of applications. In this work, we will introduce our research on a bio-inspired neuromorphic embedded computing engine named Centaur, which aims to achieve an ultra-high power efficiency beyond One-TeraFlops-Per-Watt by adopting the bio-inspired computation model and the advanced memristor technology. The success of Centaur design may promote the embedded system power efficiency three orders of magnitude from the current level while the small footprint and real-time re-configurability of the design allow an easy integration into MPSoCs, enabling many emerging mobile and embedded applications.
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