Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines

作者： Shaofu Xu, Jing Wang, and Wiwen Zou*
摘要：Recent progress on optical neural networks heralds a new future for efficient deep learning accelerators, and novel architectures of optical convolutional neural networks provide potential solutions to the widely adopted convolutional models. In so-far optical convolutional neural networks, the data patching (a necessary process in the convolutional layer) is mostly executed with electronics, resulting in a demand for large input modulator arrays. Here, we experimentally demonstrate an optical patching scheme to release the burden of electronic data processing and to cut down the scale of input modulator array for optical convolutional neural networks. Optical delay lines replace electronics to execute data processing, which can reduce the scale of input modulator array. The adoption of wavelength-division multiplexing enables a single group of optical delay lines to simultaneously process multiple input data, reducing the system complexity. The optical patching scheme provides a new solution to the problem of data input, which is challenging and concerned in the field of optical neural networks.
出版源：Optics Letters
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