Journal
OPTICS EXPRESS
Volume 27, Issue 14, Pages 19778-19787Publisher
Optica Publishing Group
DOI: 10.1364/OE.27.019778
Keywords
-
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61822508, 61571292, 61535006]
Ask authors/readers for more resources
Optical neural networks (ONNs) have become competitive candidates for the next generation of high-performance neural network accelerators because of their low power consumption and high-speed nature. Beyond fully-connected neural networks demonstrated in pioneer works, optical computing hardwares can also conduct convolutional neural networks (CNNs) by hardware reusing. Following this concept, we propose an optical convolution unit (OCU) architecture. By reusing the OCU architecture with different inputs and weights, convolutions with arbitrary input sizes can be done. A proof-of-concept experiment is carried out by cascaded acousto-optical modulator arrays. When the neural network parameters are ex-situ trained, the OCU conducts convolutions with SDR up to 28.22 dBc and performs well on inferences of typical CNN tasks. Furthermore, we conduct in situ training and get higher SDR at 36.27 dBc, verifying the OCU could be further refined by in situ training. Besides the effectiveness and high accuracy, the simplified OCU architecture served as a building block could be easily duplicated and integrated to future chip-scale optical CNNs. (c) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available