Transferable polychromatic optical encoder for neural networks

• URL: http://arxiv.org/abs/2411.02697v1
• Date: Tue, 05 Nov 2024 00:49:47 GMT
• Title: Transferable polychromatic optical encoder for neural networks
• Authors: Minho Choi, Jinlin Xiang, Anna Wirth-Singh, Seung-Hwan Baek, Eli Shlizerman, Arka Majumdar,
• Abstract summary: In this paper, we demonstrate an optical encoder that can perform convolution simultaneously in three color channels during the image capture. Such an optical encoding results in 24,000 times reduction in computational operations, with a state-of-the art classification accuracy (73.2%) in free-space optical system.
• Score: 13.311727599288524
• License:
• Abstract: Artificial neural networks (ANNs) have fundamentally transformed the field of computer vision, providing unprecedented performance. However, these ANNs for image processing demand substantial computational resources, often hindering real-time operation. In this paper, we demonstrate an optical encoder that can perform convolution simultaneously in three color channels during the image capture, effectively implementing several initial convolutional layers of a ANN. Such an optical encoding results in ~24,000 times reduction in computational operations, with a state-of-the art classification accuracy (~73.2%) in free-space optical system. In addition, our analog optical encoder, trained for CIFAR-10 data, can be transferred to the ImageNet subset, High-10, without any modifications, and still exhibits moderate accuracy. Our results evidence the potential of hybrid optical/digital computer vision system in which the optical frontend can pre-process an ambient scene to reduce the energy and latency of the whole computer vision system.

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