Photonic Neuromorphic Accelerator for Convolutional Neural Networks based on an Integrated Reconfigurable Mesh
arxiv(2024)
摘要
In this work, we present and experimentally validate a passive
photonic-integrated neuromorphic accelerator that uses a hardware-friendly
optical spectrum slicing technique through a reconfigurable silicon photonic
mesh. The proposed scheme acts as an analogue convolutional engine, enabling
information preprocessing in the optical domain, dimensionality reduction and
extraction of spatio-temporal features. Numerical results demonstrate that
utilizing only 7 passive photonic nodes, critical modules of a digital
convolutional neural network can be replaced. As a result, a 98.6
the MNIST dataset was achieved, with a power consumption reduction of at least
26
achieving 97.7
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