Energy-Efficient and High-Throughput Nanophotonic Neuromorphic Computing

M. Nazirzadeh, M. Shamsabardeh, S. Yoo
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引用次数: 2

Abstract

High-efficient bio-inspired nanophotonic neuromorphic computing is discussed. Using reconfigurable photonic interconnects combined by optical 2D and 3D integrated circuits, more than 500× improvement in energy consumption is predicted compared to the state-of-the-art implementations of SNNs.
节能和高通量纳米光子神经形态计算
讨论了高效的仿生纳米光子神经形态计算。使用由光学2D和3D集成电路组合的可重构光子互连,与最先进的snn实现相比,预计能耗将提高500倍以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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