Integrated Photonic Accelerator Based on Optical Spectrum Slicing for Convolutional Neural Networks

Autor:	Tsirigotis, Aris, Sarantoglou, George, Deligiannidis, Stavros, Sozos, Kostas, Bogris, Adonis, Mesaritakis, Charis
Rok vydání:	2023
Předmět:	Computer Science - Emerging Technologies Electrical Engineering and Systems Science - Image and Video Processing Physics - Optics
Druh dokumentu:	Working Paper
Popis:	In this work we numerically analyze a passive photonic integrated neuromorphic accelerator based on hardware-friendly optical spectrum slicing nodes. The proposed scheme can act as a fully analogue convolutional layer, preprocessing information directly in the optical domain. The proposed scheme allows the extraction of meaningful spatio-temporal features from the incoming data, thus when used prior to a simple fully connected digital single layer network it can boost performance with negligible power consumption. Numerical simulations using the MNIST dataset confirmed the acceleration properties of the proposed scheme, where 10 neuromorphic nodes can replace the convolutional layers of a sophisticated LeNet-5 network, thus reducing the number of total floating point operations per second (FLOPS) by 98% while offering a 97.2% classification accuracy. Comment: 4 pages, 3 figures, submitted Optics Letters
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.10357 Zobrazit plný text záznamu View this record from Arxiv