Abstract
AI is fueling explosive growth in compute demand that traditional digital chip architectures cannot keep up with. Analog crossbar arrays enable power efficient synaptic signal processing with linear scaling on neural network size. We present a photonic photorefractive crossbar array for neural network training and inference on local analog memory. We discuss the concept and present results based on the first prototype hardware.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
7 articles.
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