Loading [MathJax]/extensions/MathZoom.js
A Winograd-Based Integrated Photonics Accelerator for Convolutional Neural Networks | IEEE Journals & Magazine | IEEE Xplore

A Winograd-Based Integrated Photonics Accelerator for Convolutional Neural Networks


Abstract:

Neural Networks (NNs) have become the mainstream technology in the artificial intelligence (AI) renaissance over the past decade. Among different types of neural networks...Show More

Abstract:

Neural Networks (NNs) have become the mainstream technology in the artificial intelligence (AI) renaissance over the past decade. Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved leading results in many fields such as computer vision and speech recognition. This success in part is due to the widespread availability of capable underlying hardware platforms. In parallel, hardware specialization can expose us to novel architectural solutions, which can outperform general purpose computers for the tasks at hand. Although different applications demand for different performance measures, they all share speed and energy efficiency as high priorities. Meanwhile, photonics processing has seen a resurgence due to its inherited high speed and low power nature. Here, we investigate the potential of using photonics in CNNs by proposing a CNN accelerator design based on Winograd filtering algorithm. Our evaluation results show that while a photonic accelerator can compete with current state-of-the-art electronic platforms in terms of both speed and power, it has the potential to improve the energy efficiency by up to three orders of magnitude.
Published in: IEEE Journal of Selected Topics in Quantum Electronics ( Volume: 26, Issue: 1, Jan.-Feb. 2020)
Article Sequence Number: 6100312
Date of Publication: 03 December 2019

ISSN Information:

Funding Agency:


References

References is not available for this document.