[1] G. E. Moore. Cramming more components onto integrated circuits. Proc. IEEE, 86, 82-85(1998).

[2] H. J. Caulfield, S. Dolev. Why future supercomputing requires optics. Nat. Photonics, 4, 261-263(2010).

[3] K. Kitayama et al. Novel frontier of photonics for data processing—photonic accelerator. APL Photonics, 4, 090901(2019).

[4] R. P. Bocker. Matrix multiplication using incoherent optical techniques. Appl. Opt., 13, 1670-1676(1974).

[5] J. W. Goodman, A. Dias, L. Woody. Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms. Opt. Lett., 2, 1-3(1978).

[6] R. A. Athale, W. C. Collins. Optical matrix–matrix multiplier based on outer product decomposition. Appl. Opt., 21, 2089-2090(1982).

[7] M. Gruber, J. Jahns, S. Sinzinger. Planar-integrated optical vector-matrix multiplier. Appl. Opt., 39, 5367-5373(2000).

[8] Y. Owechko et al. Holographic associative memory with nonlinearities in the correlation domain. Appl. Opt., 26, 1900-1910(1987).

[9] E. G. Paek, D. Psaltis. Optical associative memory using Fourier transform holograms. Opt. Eng., 26, 428-433(1987).

[10] M. Ishikawa et al. Optical associatron: a simple model for optical associative memory. Appl. Opt., 28, 291-301(1989).

[11] J. Tanida, Y. Ichioka. Optical logic array processor using shadowgrams. J. Opt. Soc. Am., 73, 800-809(1983).

[12] Y. Ichioka, J. Tanida. Optical parallel logic gates using a shadow-casting system for optical digital computing. Proc. IEEE, 72, 787-801(1984).

[13] J. Tanida, Y. Ichioka. OPALS: optical parallel array logic system. Appl. Opt., 25, 1565-1570(1986).

[14] K.-H. Brenner, A. Huang, N. Streibl. Digital optical computing with symbolic substitution. Appl. Opt., 25, 3054-3060(1986).

[15] D. A. Miller. Self-configuring universal linear optical component. Photonics Res., 1, 1-15(2013).

[16] Y. Shen et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics, 11, 441-446(2017).

[17] N. C. Harris et al. Linear programmable nanophotonic processors. Optica, 5, 1623-1631(2018).

[18] M. Zhang, L. Wang, P. Ye. All optical XOR logic gates: technologies and experiment demonstrations. IEEE Commun. Mag., 43, S19-S24(2005).

[19] Y. Zhang, Y. Zhang, B. Li. Optical switches and logic gates based on self-collimated beams in two-dimensional photonic crystals. Opt. Express, 15, 9287-9292(2007).

[20] Y.-D. Wu, T.-T. Shih, M.-H. Chen. New all-optical logic gates based on the local nonlinear Mach-Zehnder interferometer. Opt. Express, 16, 248-257(2008).

[21] K.-S. Lee, S.-K. Kim. Conceptual design of spin wave logic gates based on a Mach–Zehnder-type spin wave interferometer for universal logic functions. J. Appl. Phys., 104, 053909(2008).

[22] J. Dong, X. Zhang, D. Huang. A proposal for two-input arbitrary Boolean logic gates using single semiconductor optical amplifier by picosecond pulse injection. Opt. Express, 17, 7725-7730(2009).

[23] Y. Fu, X. Hu, Q. Gong. Silicon photonic crystal all-optical logic gates. Phys. Lett. A, 377, 329-333(2013).

[24] D. G. Sankar Rao, S. Swarnakar, S. Kumar. Performance analysis of all-optical NAND, NOR, and XNOR logic gates using photonic crystal waveguide for optical computing applications. Opt. Eng., 59, 057101(2020).

[25] Y. Xie et al. Programmable optical processor chips: toward photonic RF filters with DSP-level flexibility and MHz-band selectivity. Nanophotonics, 7, 421-454(2017).

[26] W. Bogaerts et al. Programmable photonic circuits. Nature, 586, 207-216(2020).

[27] Z. Ying et al. Electronic-photonic arithmetic logic unit for high-speed computing. Nat. Commun., 11, 2154(2020).

[28] X. Lin et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

[29] T. Yan et al. Fourier-space diffractive deep neural network. Phys. Rev. Lett., 123, 023901(2019).

[30] J. Weng et al. Meta-neural-network for real-time and passive deep-learning-based object recognition. Nat. Commun., 11, 6309(2020).

[31] H. Chen et al. Diffractive deep neural networks at visible wavelengths. Engineering, 7, 1483-1491(2021).

[32] H. Zhu et al. Space-efficient optical computing with an integrated chip diffractive neural network. Nat. Commun., 13, 1044(2022).

[33] C. Qian et al. Dynamic recognition and mirage using neuro-metamaterials. Nat. Commun., 13, 2694(2022).

[34] Y. Luo et al. Computational imaging without a computer: seeing through random diffusers at the speed of light. elight, 2, 4(2022).

[35] Ç. Işıl et al. Super-resolution image display using diffractive decoders. Sci. Adv., 8, eadd3433(2022).

[36] M. Huang et al. Diffraction neural network for multi-source information of arrival sensing. Laser Photonics Rev., 17, 2300202(2023).

[37] T. Igarashi, M. Naruse, R. Horisaki. Incoherent diffractive optical elements for extendable field-of-view imaging. Opt. Express, 31, 31369-31382(2023).

[38] O. Kulce et al. All-optical information-processing capacity of diffractive surfaces. Light Sci. Appl., 10, 25(2021).

[39] J. Li et al. Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network. Light Sci. Appl., 11, 153(2022).

[40] J. Li et al. Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network. Adv. Photonics, 5, 016003(2023).

[41] C. Qian et al. Performing optical logic operations by a diffractive neural network. Light Sci. Appl., 9, 59(2020).

[42] P. Wang et al. Orbital angular momentum mode logical operation using optical diffractive neural network. Photonics Res., 9, 2116-2124(2021).

[43] S. Zarei, A. Khavasi. Realization of optical logic gates using on-chip diffractive optical neural networks. Sci. Rep., 12, 15747(2022).

[44] Y. Luo, D. Mengu, A. Ozcan. Cascadable all-optical NAND gates using diffractive networks. Sci. Rep., 12, 7121(2022).

[45] X. Liu et al. Parallelized and cascadable optical logic operations by few-layer diffractive optical neural network. Photonics, 10, 503(2023).

[46] X. Ding et al. Metasurface-based optical logic operators driven by diffractive neural networks. Adv. Mater., 36, 2308993(2024).

[47] X. Lin et al. Polarization-based all-optical logic gates using diffractive neural networks. J. Opt., 26, 035701(2024).

[48] M. Sonka, V. Hlavac, R. Boyle. Image Processing, Analysis and Machine Vision(2013).

[49] J. A. Dev. Bitcoin mining acceleration and performance quantification, 1-6(2014).

[50] A. Reuther et al. Survey and benchmarking of machine learning accelerators, 1-9(2019).

[51] J. D. Owens et al. GPU computing. Proc. IEEE, 96, 879-899(2008).

[52] N. P. Jouppi et al. In-datacenter performance analysis of a tensor processing unit, 1-12(2017).

[53] J. Rose, A. El Gamal, A. Sangiovanni-Vincentelli. Architecture of field-programmable gate arrays. Proc. IEEE, 81, 1013-1029(1993).

[54] X. Zhang et al. Machine learning on FPGAs to face the IoT revolution, 894-901(2017).

[55] K. R. Hari et al. Cryptocurrency mining–transition to cloud. Int. J. Adv. Comput. Sci. Appl., 6, 115-124(2015).

[56] E. Nurvitadhiet al.,. Accelerating recurrent neural networks in analytics servers: comparison of FPGA, CPU, GPU, and ASIC, 1-4(2016).

[57] J. W. Goodman. Introduction to Fourier Optics(2005).

[58] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2014).

[59] M. Courbariaux, Y. Bengio, J.-P. David. BinaryConnect: training deep neural networks with binary weights during propagations(2015).

[60] H. Wang et al. Toward near-perfect diffractive optical elements via nanoscale 3D printing. ACS Nano, 14, 10452-10461(2020).

[61] Z. Zhang et al. Space-time projection enabled ultrafast all-optical diffractive neural network. Laser and Photonics Rev., 18, 2301367(2024).

[62] J. Zhou, H. Pu, J. Yan. Spatiotemporal diffractive deep neural networks. Opt. Express, 32, 1864-1877(2024).

[63] Z. Duan, H. Chen, X. Lin. Optical multi-task learning using multi-wavelength diffractive deep neural networks. Nanophotonics, 12, 893-903(2023).

[64] X. Luo et al. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light Sci. Appl., 11, 158(2022).

[65] D. Mengu et al. Misalignment resilient diffractive optical networks. Nanophotonics, 9, 4207-4219(2020).

[66] M. Nakajima et al. Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware. Nat. Commun., 13, 7847(2022).

[67] A. Momeni et al. Training of physical neural networks(2024).

[68] K. Matsushima. Formulation of the rotational transformation of wave fields and their application to digital holography. Appl. Opt., 47, D110-D116(2008).

[69] N. K. Fontaine et al. Laguerre-Gaussian mode sorter. Nat. Commun., 10, 1865(2019).

[70] Q. Wang et al. Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks. Light Sci. Appl., 13, 10(2024).

[71] M. Yildirim et al. Nonlinear processing with linear optics. Nat. Photonics, 7(2024).