With the swift advancement of neural networks and their expanding applications in many fields, optical neural networks have gradually become a feasible alternative to electrical neural networks due to their parallelis...With the swift advancement of neural networks and their expanding applications in many fields, optical neural networks have gradually become a feasible alternative to electrical neural networks due to their parallelism, high speed, low latency, and power consumption. Nonetheless, optical nonlinearity is hard to realize in free-space optics, which restricts the potential of the architecture.展开更多
Machine vision faces bottlenecks in computing power consumption and large amounts of data.Although opto-electronic hybrid neural networks can provide assistance,they usually have complex structures and are highly depe...Machine vision faces bottlenecks in computing power consumption and large amounts of data.Although opto-electronic hybrid neural networks can provide assistance,they usually have complex structures and are highly dependent on a coherent light source;therefore,they are not suitable for natural lighting environment applications.In this paper,we propose a novel lensless opto-electronic neural network architecture for machine vision applications.The architecture optimizes a passive optical mask by means of a task-oriented neural network design,performs the optical convolution calculation operation using the lensless architecture,and reduces the device size and amount of calculation required.We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21%were achieved.Furthermore,we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition,thereby obtaining the same recognition accuracy performance as no-encryption methods.Compared with the random MLS pattern,the recognition accuracy is improved by more than 6%.展开更多
基金National Natural Science Foundation of China(62135009)Beijing Municipal Science and Technology Commission,Administrative Commission of Zhongguancun Science Park (Z221100005322010)。
文摘With the swift advancement of neural networks and their expanding applications in many fields, optical neural networks have gradually become a feasible alternative to electrical neural networks due to their parallelism, high speed, low latency, and power consumption. Nonetheless, optical nonlinearity is hard to realize in free-space optics, which restricts the potential of the architecture.
基金The authors wish to acknowledge the support of the National Natural Science Foundation of China(62135009)the National Key Research and Development Program of China(2019YFB1803500)the Institute for Guo Qiang Tsinghua University.
文摘Machine vision faces bottlenecks in computing power consumption and large amounts of data.Although opto-electronic hybrid neural networks can provide assistance,they usually have complex structures and are highly dependent on a coherent light source;therefore,they are not suitable for natural lighting environment applications.In this paper,we propose a novel lensless opto-electronic neural network architecture for machine vision applications.The architecture optimizes a passive optical mask by means of a task-oriented neural network design,performs the optical convolution calculation operation using the lensless architecture,and reduces the device size and amount of calculation required.We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21%were achieved.Furthermore,we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition,thereby obtaining the same recognition accuracy performance as no-encryption methods.Compared with the random MLS pattern,the recognition accuracy is improved by more than 6%.
