Photonic In-Memory Computing Primitive for Spiking Neural Networks Using Phase-Change Materials
Indranil ChakrabortySchool of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907, USAGobinda SahaSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907, USAKaushik RoySchool of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907, USA
2019en
ABI
Abstract
The recent demonstration of ultrafast photonic computing devices exhibiting optical switching of phase-change materials has piqued interest in their viability for neuromorphic computing, but scaling these standalone devices to parallel computing platforms poses a major challenge. The authors leverage the parallelism offered by wavelength-division multiplexing (WDM) to propose a photonic ``in-memory'' platform, which can be used to emulate a spiking neural network. This solution could potentially bridge the gap between isolated computing devices and large-scale implementations of neuromorphic systems.
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