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Digital-analog Hybrid Matrix Multiplication Processor for Optical Neural Networks

Computing Research Repository (CoRR)(2024)

Technical University of Denmark DTU Electro

Cited 0|Views30
Abstract
The computational demands of modern AI have spurred interest in optical neural networks (ONNs) which offer the potential benefits of increased speed and lower power consumption. However, current ONNs face various challenges,most significantly a limited calculation precision (typically around 4 bits) and the requirement for high-resolution signal format converters (digital-to-analogue conversions (DACs) and analogue-to-digital conversions (ADCs)). These challenges are inherent to their analog computing nature and pose significant obstacles in practical implementation. Here, we propose a digital-analog hybrid optical computing architecture for ONNs, which utilizes digital optical inputs in the form of binary words. By introducing the logic levels and decisions based on thresholding, the calculation precision can be significantly enhanced. The DACs for input data can be removed and the resolution of the ADCs can be greatly reduced. This can increase the operating speed at a high calculation precision and facilitate the compatibility with microelectronics. To validate our approach, we have fabricated a proof-of-concept photonic chip and built up a hybrid optical processor (HOP) system for neural network applications. We have demonstrated an unprecedented 16-bit calculation precision for high-definition image processing, with a pixel error rate (PER) as low as $1.8\times10^{-3}$ at an signal-to-noise ratio (SNR) of 18.2 dB. We have also implemented a convolutional neural network for handwritten digit recognition that shows the same accuracy as the one achieved by a desktop computer. The concept of the digital-analog hybrid optical computing architecture offers a methodology that could potentially be applied to various ONN implementations and may intrigue new research into efficient and accurate domain-specific optical computing architectures for neural networks.
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Optical Performance Monitoring,Digital Signal Processing,Optoelectronic Reservoir Computing,Neuromorphic Photonics,Optical Modulators
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2017

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要点】:本文提出了一种数字-模拟混合的光学神经网络计算体系结构,利用二进制词的数字光学输入来提高计算精度,并解决了有限计算精度和高分辨率信号格式转换的问题。

方法】:通过引入门槛逻辑和决策,可以显著提高计算精度,并可以移除输入数据的DAC并大大降低ADC的分辨率。

实验】:通过制作样品光子芯片,并构建一个混合光学处理器(HOP)系统,证明了本方法在高清图像处理方面有16位计算精度,PER仅为1.8×10^-3,SNR为18.2 dB。同时,还在手写数字识别中实现了卷积神经网络,其准确性与桌面电脑相同。该数字-模拟混合光学计算架构的概念可以应用于各种ONN实现,并可能引起对于神经网络高效准确领域特定光学计算架构的新研究的兴趣。