热点文献带您关注AI在光神经网络领域的最新进展——图书馆前沿文献专题推荐服务（53）

2021-11-29

 

        在上一期AI文献推荐中，我们为您推荐了人工智能在集成电路领域的热点文献,包括使用纳米级CMOS工艺在同一晶片上协同集成单晶体管神经元和突触来制造高度可扩展的类脑神经拟态硬件，用于高性能神经形态计算和神经网络剪枝的基于拓扑相变的模拟记忆突触，一种由包含片上内存的计算芯片网络组成的DNN推理系统，基于二维半导体光电二极管阵列神经网络图像传感器的超快机器视觉等方面的文献。
        本期我们为您选取了4篇文献，介绍AI在光神经网络领域的热点文献，包括用于光学神经网络的11-TOPS光子卷积加速器，用于光纤非线性补偿的硅光子神经网络，用于人工智能和神经形态计算的光子学，一种实现复值神经网络的光学神经芯片等文献，推送给相关领域的科研人员。
文献一 用于光学神经网络的11-TOPS光子卷积加速器
11 TOPS photonic convolutional accelerator for optical neural networks
Xu, Xingyuan, etc.
NATURE, 2021, 589(7840): 44-51
Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (10¹²) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels-sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition.
阅读原文  https://www.nature.com/articles/s41586-020-03063-0
 
文献二 用于光纤非线性补偿的硅光子神经网络
A silicon photonic–electronic neural network for fibre nonlinearity compensation
Huang, Chaoren, etc.
Nature Electronics, 2021
In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonic–electronic neural network for solving fibre nonlinearity compensation in submarine optical-fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a silicon photonic platform compatible with complementary metal–oxide–semiconductor technology. We show that the platform can be used to compensate for optical fibre nonlinearities and improve the quality factor of the signal in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a workstation assisted with a 32-bit graphic processing unit.
阅读原文  https://www.nature.com/articles/s41928-021-00661-2

文献三 用于人工智能和神经形态计算的光子学
Photonics for artificial intelligence and neuromorphic computing
Shastri, Bhavin J., etc.
NATURE PHOTONICS, 2021, 15(2): 102-114
Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.
阅读原文  https://www.nature.com/articles/s41566-020-00754-y
文献四 一种实现复值神经网络的光学神经芯片
An optical neural chip for implementing complex-valued neural network
Zhang, H., etc.
NATURE COMMUNICATIONS, 2021, 12(1)
Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart. Most demonstrations of optical neural networks for computing have been so far limited to real-valued frameworks. Here, the authors implement complex-valued operations in an optical neural chip that integrates input preparation, weight multiplication and output generation within a single device.
阅读原文 https://www.nature.com/articles/s41467-020-20719-7
 
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