神经形态工程学
记忆电阻器
计算机科学
实现(概率)
光子学
带宽(计算)
计算机体系结构
钥匙(锁)
电子工程
人工神经网络
人工智能
电信
工程类
物理
光电子学
统计
数学
计算机安全
作者
Alexandros Emboras,Alessandro Alabastri,Paul Lehmann,Kevin Portner,Christoph Weilenmann,Ping Ma,Bojun Cheng,Mila Lewerenz,Elias Passerini,Ueli Koch,Jan Aeschlimann,Fabian Ducry,J. Leuthold,Mathieu Luisier
摘要
Memristive-based electro-optical neuromorphic hardware takes advantage of both the high-density of electronic circuits and the high bandwidth of their photonic counterparts, thus showing potential for low-power artificial intelligence applications. In this Perspective paper, we introduce a class of electro-optical memristors that can emulate the key properties of synapses and neurons, which are essential features for the realization of electro-optical neuromorphic functionalities. We then describe the challenges associated with existing technologies and finally give our viewpoint on possible developments toward an energy-efficient neuromorphic platform.
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