徐绍夫的工作——High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays的相关研究成果近期发表在Optics Express杂志上，该工作得到了国家自然科学基金的部分资助 (grant no. 61822508, 61571292 and 61535006)。
摘要：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.