Noise quantization simulation analysis of optical convolutional networks

• Autorzy: Zhang Ye , Zhang Saining , Zhang Danni , Su Yanmei , Yi Junkai , Wang Pengfei , Wang Ruiting , Luo Guangzhen , Zhou Xuliang , Pan Jiaoqing

Identyfikatory

DOI

10.37190/oa230311

• Języki publikacji: EN

Abstrakty

EN

Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.

Słowa kluczowe

EN

optical neural network; convolutional neural network; noise; quantization

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2023
• Tom: Vol. 53, nr 3
• Strony: 483--493
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Zhang Ye

• School of Automation, Beijing Information Science and Technology University, Beijing, China

autor

Zhang Saining

• School of Computer Science Technology, Beijing Institute of Technology, Beijing, China

autor

Zhang Danni

• School of Automation, Beijing Information Science and Technology University, Beijing, China
• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

autor

Su Yanmei

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

autor

Yi Junkai

• School of Automation, Beijing Information Science and Technology University, Beijing, China

autor

Wang Pengfei

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

autor

Wang Ruiting

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

autor

Luo Guangzhen

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

autor

Zhou Xuliang

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

autor

Pan Jiaoqing

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

Bibliografia

• [1] KRIZHEVSKY A., SUTSKEVER I., HINTON G.E., ImageNet classification with deep convolutional neural networks, Communications of the ACM 60(6), 2017: 84-90. https://doi.org/10.1145/3065386
• [2] ACHARYA U.R., OH S.L., HAGIWARA Y., TAN J.H., ADAM M., GERTYCH A., TAN R.S., A deep convolutional neural network model to classify heartbeats, Computers in Biology and Medicine 89, 2017: 389-396. https://doi.org/10.1016/j.compbiomed.2017.08.022
• [3] SHELHAMER E., LONG J., DARRELL T., Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4), 2017: 640-651. https://doi.org/ 10.1109/TPAMI.2016.2572683
• [4] SOKOLOV A.S., ABBAS H., ABBAS Y., CHOI C., Towards engineering in memristors for emerging memory and neuromorphic computing: A review, Journal of Semiconductors 42(1), 2021: 013101. https://doi.org/10.1088/1674-4926/42/1/013101
• [5] PARK S., NOH J., CHOO M.-I., SHERI A. M., CHANG M., KIM Y.-B., KIM C. J., JEON M., LEE B.-G., LEE B.H., HWANG H., Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device, Nanotechnology 24(38), 2013: 384009. https://doi.org/10.1088/0957-4484/24/38/384009
• [6] YAO P., WU H., GAO B., TANG J., ZHANG Q., ZHANG W., YANG J.J., QIAN H., Fully hardware-implemented memristor convolutional neural network, Nature 577(7792), 2020: 641-646. https://doi.org/10.1038/s41586-020-1942-4
• [7] PARK S., HONG I., PARK J., YOO H.-J., An energy-efficient embedded deep neural network processor for high speed visual attention in mobile vision recognition SoC, IEEE Journal of Solid-State Circuits 51(10), 2016: 2380-2388. https://doi.org/10.1109/JSSC.2016.2582864
• [8] LARGER L., BAYLÓN-FUENTES A., MARTINENGHI R., UDALTSOV V. S., CHEMBO Y.K., JACQUOT M., High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification, Physical Review X 7(1), 2017: 011015. https://doi.org/10.1103/PhysRevX.7.011015
• [9] PENG H.-T., NAHMIAS M.A., DE LIMA T.F., TAIT A.N., SHASTRI B.J., Neuromorphic photonic integrated circuits, IEEE Journal of Selected Topics in Quantum Electronics 24(6), 2018: 6101715. https://doi.org/10.1109/JSTQE.2018.2840448
• [10] LIN X., RIVENSON Y., YARDIMCI N.T., VELI M., LUO Y., JARRAHI M., OZCAN A., All-optical machine learning using diffractive deep neural networks, Science 361(6406), 2018: 1004-1008. https://doi.org/10.1126/science.aat8084
• [11] SHEN Y., HARRIS N. C., SKIRLO S., PRABHU M., BAEHR-JONES T., HOCHBERG M., SUN X., ZHAO S., LAROCHELLE H., ENGLUND D., SOLJAČIĆ M., Deep learning with coherent nanophotonic circuits,Nature Photonics 11(7), 2017: 441-446. https://doi.org/10.1038/nphoton.2017.93
• [12] XU X., TAN M., CORCORAN B., WU J., BOES A., NGUYEN T.G., CHU S.T., LITTLE B.E., HICKS D.G., MORANDOTTI R., MITCHELL A., MOSS D.J., 11 TOPS photonic convolutional accelerator for optical neural networks, Nature 589(7840), 2021: 44-51. https://doi.org/10.1038/s41586-020-03063-0
• [13] CHANG J., SITZMANN V., DUN X., HEIDRICH W., WETZSTEIN G., Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification, Scientific Reports 8(1), 2018: 12324. https://doi.org/10.1038/s41598-018-30619-y
• [14] GU J., ZHAO Z., FENG C., ZHU H., CHEN R.T., PAN D.Z., ROQ: A noise-aware quantization scheme towards robust optical neural networks with low-bit controls, [In] 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France, 2020: 1586-1589. https://doi.org/ 10.23919/DATE48585.2020.9116521
• [15] WILLIAMSON I.A.D., HUGHES T.W., MINKOV M., BARTLETT B., PAI S., FAN S., Reprogrammable electro-optic nonlinear activation functions for optical neural networks, IEEE Journal of Selected Topics in Quantum Electronics 26(1), 2020: 7700412. https://doi.org/10.1109/JSTQE.2019.2930455
• [16] FANG M.Y.-S., MANIPATRUNI S., WIERZYNSKI C., KHOSROWSHAHI A., DEWEESE M.R., Design of optical neural networks with component imprecisions, Optics Express 27(10), 2019: 14009-14029. https://doi.org/10.1364/OE.27.014009
• [17] SLUSSARENKO S., WESTON M.M., CHRZANOWSKI H.M., SHALM L.K., VERMA V.B., NAM S.W., PRYDE G.J., Unconditional violation of the shot-noise limit in photonic quantum metrology, Nature Photonics 11(11), 2017: 700-703. https://doi.org/10.1038/s41566-017-0011-5
• [18] HARRIS N.C., MA Y., MOWER J., BAEHR-JONES T., ENGLUND D., HOCHBERG M., GALLAND C., Efficient, compact and low loss thermo-optic phase shifter in silicon, Optics Express 22(9), 2014: 10487-10493. https://doi.org/10.1364/OE.22.010487
• [19] TAIT A.N., NAHMIAS M.A., SHASTRI B.J., PRUCNAL P.R., Broadcast and weight: An integrated network for scalable photonic spike processing, Journal of Lightwave Technology 32(21), 2014: 3427-3439.
• [20] ZHANG D., ZHANG Y., ZHANG Y., SU Y., YI J., WANG P., WANG R., LUO G., ZHOU X., PAN J., Training and inference of optical neural networks with noise and low-bits control, Applied Sciences 11(8), 2021: 3692. https://doi.org/10.3390/app11083692
• [21] KIM J.-Y., KANG J.-M., KIM T.-Y., HAN S.-K., 10 Gbit/s all-optical composite logic gates with XOR, NOR, OR and NAND functions using SOA-MZI structures, Electronics Letters 42(5), 2006: 303-304. https://doi.org/10.1049/el:20063501
• [22] ZHANG D., WANG P., LUO G., BI Y., ZHANG Y., YI J., SU Y., ZHANG Y., PAN J., Design of a silicon-based optical neural network, Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019), Advances in Computer Science Research, Vol. 93, 2019: 184-186.
• [23] SPRINGER P., BIENTINESI P., Design of a high-performance GEMM-like tensor–tensor multiplication, ACM Transactions on Mathematical Software 44(3), 2018: 28. https://doi.org/10.1145/3157733
• [24] LAWSON C.L., HANSON R.J., KINCAID D.R., KROGH F.T., Basic linear algebra subprograms for Fortran usage, ACM Transactions on Mathematical Software 5(3), 1979: 308-323. https://doi.org/10.1145/355841.355847
• [25] VASUDEVAN A., ANDERSON A., GREGG D., Parallel multi channel convolution using general matrix multiplication, [In] 2017 IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP), Seattle, WA, USA, 2017: 19-24. https://doi.org/10.1109/ASAP.2017.7995254
• [26] KURZAK J., TOMOV S., DONGARRA J., Autotuning GEMM kernels for the Fermi GPU, IEEE Transactions on Parallel and Distributed Systems 23(11), 2012: 2045-2057. https://doi.org/10.1109/TPDS.2011.311
• [27] BARRACHINA S., DOLZ M.F., SAN JUAN P., QUINTANA-ORTÍ E.S., Efficient and portable GEMM-based convolution operators for deep neural network training on multicore processors, Journal of Parallel and Distributed Computing 167, 2022: 240-254. https://doi.org/10.1016/j.jpdc.2022.05.009
• [28] HAMERLY R., BERNSTEIN L., SLUDDS A., SOLJAČIĆ M., ENGLUND D., Large-scale optical neural networks based on photoelectric multiplication, Physical Review X 9(2), 2019: 021032. https://doi.org/10.1103/PhysRevX.9.021032
• [29] MOREN K., GÖHRINGER D., A framework for accelerating local feature extraction with OpenCL on multi-core CPUs and co-processors, Journal of Real-Time Image Processing 16(4), 2019: 901-918. https://doi.org/10.1007/s11554-016-0576-0
• [30] WU J., LENG C., WANG Y., HU Q., CHENG J., Quantized convolutional neural networks for mobile devices, [In] 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 4820-4828. https://doi.org/10.1109/CVPR.2016.521
• [31] GUPTA S., AGRAWAL A., GOPALAKRISHNAN K., NARAYANAN P., Deep learning with limited numerical precision, Proceedings of the 32nd International Conference on Machine Learning, 2015: 1737-1746.