交叉相关CNN的模拟光学模式识别。

IF 1.9 4区 工程技术 Q3 MICROSCOPY
Ahmed Farhat, Wim J C Melis
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引用次数: 0

摘要

卷积神经网络(cnn)中的模式识别依赖于二维卷积,计算量大,需要大量的处理能力和时间。本文提出了一种模拟光学硬件系统来提高CNN的效率,重点关注前向传播任务,如数据准备、相关和决策。通过利用光波的连续特性进行二维卷积运算,该系统克服了冯·诺伊曼架构在节省功率和时间方面的关键限制。光波操作允许更高效和即时的任务,如二维傅里叶变换,这对模式识别至关重要。本文通过MATLAB和COMSOL仿真对这些概念进行了验证。总的来说,所提出的方法为更高效的机器学习硬件铺平了道路。未来的工作将集中在扩展系统以实现完整的CNN训练,包括反向传播,以及开发商业上合适的硬件实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analogue optical pattern recognition for cross-correlational CNN.

Pattern recognition in convolutional neural networks (CNNs) is computationally intensive due to its reliance on 2D convolutions, requiring significant processing power and time. This paper proposes an analogue optical hardware system to improve CNN efficiency, focusing on forward propagation tasks such as data preparation, correlation, and decision-making. By utilising the continuous properties of light waves for 2D convolutional operations, the system overcomes key limitations of von Neumann architectures around saving power and time. Optical wave operations allow for more efficient and instantaneous tasks like 2D Fourier transforms, which are crucial to pattern recognition. The paper validates these concepts through simulations using MATLAB and COMSOL. Overall, the presented approach paves the way for more efficient ML hardware. Future work will focus on extending the system to enable full CNN training, including backward propagation, as well as the development of commercially suitable hardware implementations.

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来源期刊
Journal of microscopy
Journal of microscopy 工程技术-显微镜技术
CiteScore
4.30
自引率
5.00%
发文量
83
审稿时长
1 months
期刊介绍: The Journal of Microscopy is the oldest journal dedicated to the science of microscopy and the only peer-reviewed publication of the Royal Microscopical Society. It publishes papers that report on the very latest developments in microscopy such as advances in microscopy techniques or novel areas of application. The Journal does not seek to publish routine applications of microscopy or specimen preparation even though the submission may otherwise have a high scientific merit. The scope covers research in the physical and biological sciences and covers imaging methods using light, electrons, X-rays and other radiations as well as atomic force and near field techniques. Interdisciplinary research is welcome. Papers pertaining to microscopy are also welcomed on optical theory, spectroscopy, novel specimen preparation and manipulation methods and image recording, processing and analysis including dynamic analysis of living specimens. Publication types include full papers, hot topic fast tracked communications and review articles. Authors considering submitting a review article should contact the editorial office first.
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