Digital Modeling on Large Kernel Metamaterial Neural Network

• URL: http://arxiv.org/abs/2307.11862v1
• Date: Fri, 21 Jul 2023 19:07:02 GMT
• Title: Digital Modeling on Large Kernel Metamaterial Neural Network
• Authors: Quan Liu, Hanyu Zheng, Brandon T. Swartz, Ho hin Lee, Zuhayr Asad, Ivan Kravchenko, Jason G. Valentine and Yuankai Huo
• Abstract summary: We propose a large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN. With the proposed LMNN, the cost of the convolutional front-end can be offloaded into fabricated optical hardware.
• Score: 7.248553563042369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3x3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.

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