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光学衍射神经网络(optical diffraction neural network, ODNN)以光波作为计算媒介执行神经网络的逻辑分析与运算功能, 具有高速度、低功耗及并行处理的优势. 本文设计了一种仅有三层相位调制的ODNN, 提出了基于目标空间频率一级谱分布提升ODNN的数字识别性能的方法, 经优化获得了系统最优的像素大小、衍射距离, 以及最佳的三层相位分布. 设计的ODNN对MNIST手写体数字集识别准确率达到了95.3%, 高于文献中采用五层衍射神经网络实现的准确率91.75% (Lin X, Rivenson Y, Yardimci N T, Veli M, Luo Y, Jarrahi M, Ozcan A
2018 Science 361 1004 ), 且精简了系统结构. 结合ODNN高速度、低功耗的优点, 提出的基于频谱分析方法有利于提高ODNN的性能, 使ODNN在边缘计算领域有巨大的应用潜力.Optical diffractive neural network (ODNN) uses light wave as a computing medium to perform the inference and prediction function of neural network, which has the advantages of high speed, low power consumption, and parallel processing. In this work, an ODNN with only three layers of phase modulation is designed, and a method to improve the recognition performance of ODNN based on the first-order spectral distribution of targets is proposed. Using this method, the parameters of a three-layer ODNN are effectively optimized and the optimal pixel size, diffraction distance, and method for image preprocessing are obtained. The three-layer ODNN designed in this work has a recognition accuracy rate of 95.3% for MNIST (handwritten digit set), compared with the 91.75% accuracy achieved by the five-layer ODNN in the reference (Lin X, Rivenson Y, Yardimci N T, Veli M, Luo Y, Jarrahi M, Ozcan A2018 Science 361 1004 ). In addition, the system volume is greatly reduced and the system structure is simplified. Combined with the advantages of high speed and low power consumption, it has huge potential applications in the fields such as edge computing in the future.-
Keywords:
- diffractive neural network /
- optical recognition /
- handwritten digits /
- diffractive optical elements
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图 1 (a) 深度神经网络; (b) 经典的范德鲁特光学相关器
Fig. 1. (a) Deep neural network; (b) the classic Vander Lugt correlator.
图 2 三层DOE衍射神经网络 (a) 光路示意图; (b) 程序流程图
Fig. 2. Three-layer DOE ODNN: (a) Optical path; (b) program flow chart.
图 3 接收面的区域划分及演示说明 (a) 10个阿拉伯数字所在的区域划分; (b) 当输入图像为数字8, 代表数字8的区域所在的能量最大
Fig. 3. Area division and demonstration description of the receiving surface: (a) Area division of the 10 Arabic numerals are located; (b) when the input image is the digit 8, the area representing the digit 8 has the maximum energy.
图 8 衍射距离D对ODNN识别率的影响
Fig. 8. Influence of diffraction distance D on the recognition rate of ODNN.
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[1] Yann L C, Yoshua B, Geoffrey H 2015 Nature 521 436
Google Scholar
[2] Lin X, Rivenson Y, Yardimci N T, Veli M, Luo Y, Jarrahi M, Ozcan A 2018 Science 361 1004
Google Scholar
[3] Litjens G, Kooi T, Bejnordi B E, Setio A A A, Ciompi F, Ghafoorian M, van der Laak J A W M, van Ginneken B, S´anchez C 2017 Medical Image Analysis 42 60
Google Scholar
[4] Graves A, Mohamed A, Hinton G 2013 IEEE International Conference on Acoustics 2013 6645
Google Scholar
[5] Krizhevsky A, Sutskever I, Hinton G E 2012 In Advances in NIPS 25 1097
Google Scholar
[6] Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y 2014 arXiv: 1406.1078[cs. CL]
[7] Silver D, Huang A, Maddison C J, et al. 2016 Nature 529 484
Google Scholar
[8] Shen Y, Harris N C, Skirlo S, et al. 2017 Nat. Photonics 11 441
Google Scholar
[9] Solli D R, Jalali B 2015 Nat. Photonics 9 704
Google Scholar
[10] Psaltis D, Brady D, Gu X G, Lin S 1990 Nature 343 325
Google Scholar
[11] Chang J, Sitzmann V, Dun X, Heidrich W, Wetzstein G 2018 Sci. Rep. 8 123234
[12] Yan T, Wu J, Zhou T, Xie H, Xu F, Fan J, Fang L, Lin X, Dai Q 2019 Phys. Rev. Lett. 123 023901
Google Scholar
[13] Zhou T, Fang L, Yan T, Wu J, Li Y, Fan J, Wu H, Lin X, Dai Q 2020 Photonics Research 8 940
Google Scholar
[14] Mengu D, Luo Y, Rivenson Y, Ozcan A 2020 IEEE J. Sel. Top. Quant. 26 3700114
Google Scholar
[15] Li J, Mengu D, Luo Y, Riven Y, Ozcan A 2019 Advanced Photonics 1 046001
[16] Sun Y, Dong M, Yu M, Xia J, Zhang X, Bai Y, Lu L, Zhu L 2021 International J. Optics 2021 6667495
[17] Lécun Y, Bottou L, Bengio Y, Haffner P 1998 Proceedings of the IEEE 86 2278
Google Scholar
[18] Xu P, Hong C, Cheng G, Zhou L, Sun Z 2015 Optics Express 23 6773
Google Scholar
[19] 徐海东, 徐平, 亓森林, 梁钱, 张旭琳, 黄洁锋, 黄海漩 2010 光学学报 30 1002
Google Scholar
Xu H D, Xu P, Qi S L, Liang Q, Zhang X L, Huang J F, Huang H X 2010 Acta Opt. Sin. 30 1002
Google Scholar
[20] Yang T, Chen M, Xiao Y, Xu H, Xu P 2020 IEEE Photonics J. 12 7800310
Google Scholar
[21] Xu P, Yang T, Kai L, Li X, Long S, Huang H 2018 IEEE Photonics J. 10 7801210
Google Scholar
[22] Ouabida E, Essadique A, Bouzid A 2017 Expert Systems with Applications 71 383
Google Scholar
[23] 胡文刚, 王永仲, 华文深 2006 光学技术 32 179
Google Scholar
Hu W G, Wang Y Z, Hua W S 2006 Optical Technique 32 179
Google Scholar
[24] 苏显渝, 吕乃光, 陈家璧 2010 信息光学原理 (第一版) (北京: 电子工业出版社) 第34—40页
Su X Y, Lü N G, Chen J B 2010 Principles of Information Optics (Vol. 1) (Beijing: Publishing House of Electronics Industry) pp34–40 (in Chinese)
[25] Zhang Y, Wallace B 2015 arXiv: 1510.03820 [cs. CL]
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