Optical Spiking Neurons Enable High-Speed and Energy-Efficient Optical Neural Networks
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909547653758976 |
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| author | Xu, Bo Huang, Zefeng Fang, Yuetong Wang, Xin Cheng, Bojun Yu, Shaoliang Wang, Zhongrui Xu, Renjing |
| author_facet | Xu, Bo Huang, Zefeng Fang, Yuetong Wang, Xin Cheng, Bojun Yu, Shaoliang Wang, Zhongrui Xu, Renjing |
| contents | Optical neural networks (ONNs) perform extensive computations using photons instead of electrons, resulting in passively energy-efficient and low-latency computing. Among various ONNs, the diffractive optical neural networks (DONNs) particularly excel in energy efficiency, bandwidth, and parallelism, therefore attract considerable attention. However, their performance is limited by the inherent constraints of traditional frame-based sensors, which process and produce dense and redundant information at low operating frequency. Inspired by the spiking neurons in human neural system, which utilize a thresholding mechanism to transmit information sparsely and efficiently, we propose integrating a threshold-locking method into neuromorphic vision sensors to generate sparse and binary information, achieving microsecond-level accurate perception similar to human spiking neurons. By introducing novel Binary Dual Adaptive Training (BAT) and Optically Parallel Mixture of Experts (OPMoE) inference methods, the high-speed, spike-based diffractive optical neural network (S2NN) demonstrates an ultra-fast operating speed of 3649 FPS, which is 30 fold faster than that of reported DONNs, delivering a remarkable computational speed of 417.96 TOPS and a system energy efficiency of 12.6 TOPS/W. Our work demonstrates the potential of incorporating neuromorphic architecture to facilitate optical neural network applications in real-world scenarios for both low-level and high-level machine vision tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optical Spiking Neurons Enable High-Speed and Energy-Efficient Optical Neural Networks Xu, Bo Huang, Zefeng Fang, Yuetong Wang, Xin Cheng, Bojun Yu, Shaoliang Wang, Zhongrui Xu, Renjing Optics Computational Physics Optical neural networks (ONNs) perform extensive computations using photons instead of electrons, resulting in passively energy-efficient and low-latency computing. Among various ONNs, the diffractive optical neural networks (DONNs) particularly excel in energy efficiency, bandwidth, and parallelism, therefore attract considerable attention. However, their performance is limited by the inherent constraints of traditional frame-based sensors, which process and produce dense and redundant information at low operating frequency. Inspired by the spiking neurons in human neural system, which utilize a thresholding mechanism to transmit information sparsely and efficiently, we propose integrating a threshold-locking method into neuromorphic vision sensors to generate sparse and binary information, achieving microsecond-level accurate perception similar to human spiking neurons. By introducing novel Binary Dual Adaptive Training (BAT) and Optically Parallel Mixture of Experts (OPMoE) inference methods, the high-speed, spike-based diffractive optical neural network (S2NN) demonstrates an ultra-fast operating speed of 3649 FPS, which is 30 fold faster than that of reported DONNs, delivering a remarkable computational speed of 417.96 TOPS and a system energy efficiency of 12.6 TOPS/W. Our work demonstrates the potential of incorporating neuromorphic architecture to facilitate optical neural network applications in real-world scenarios for both low-level and high-level machine vision tasks. |
| title | Optical Spiking Neurons Enable High-Speed and Energy-Efficient Optical Neural Networks |
| topic | Optics Computational Physics |
| url | https://arxiv.org/abs/2409.05726 |