{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:04:08Z","timestamp":1760058248107,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The smart-pixel-based optical convolutional neural network was proposed to improve kernel refresh rates in scalable optical convolutional neural networks (CNNs) by replacing the spatial light modulator with a smart pixel light modulator while preserving benefits such as an unlimited input node size, cascadability, and direct kernel representation. The smart pixel light modulator enhances weight update speed, enabling rapid reconfigurability. Its fast updating capability and memory expand the application scope of scalable optical CNNs, supporting operations like convolution with multiple kernel sets and difference mode. Simplifications using electrical fan-out reduce hardware complexity and costs. An evolution of this system, the smart-pixel-based bidirectional optical CNN, employs a bidirectional architecture and single lens-array optics, achieving a computational throughput of 8.3 \u00d7 1014 MAC\/s with a smart pixel light modulator resolution of 3840 \u00d7 2160. Further advancements led to the two-mirror-like smart-pixel-based bidirectional optical CNN, which emulates 2n layers using only two physical layers, significantly reducing hardware requirements despite increased time delay. This architecture was demonstrated for solving partial differential equations by leveraging local interactions as a sequence of convolutions. These advancements position smart-pixel-based optical CNNs and their derivatives as promising solutions for future CNN applications.<\/jats:p>","DOI":"10.3390\/computers14030111","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T07:59:54Z","timestamp":1742457594000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Conceptual Study of Rapidly Reconfigurable and Scalable Optical Convolutional Neural Networks Based on Free-Space Optics Using a Smart Pixel Light Modulator"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9906-1388","authenticated-orcid":false,"given":"Young-Gu","family":"Ju","sequence":"first","affiliation":[{"name":"Department of Physics Education, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. 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