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4 October 2024 Retinomorphic photonic machine vision via network lasers
Jack C. Gartside, Wai Kit Ng, Anna Fischer, Jakub C. Dranczewski, Dhruv Saxena, Tobias Farchy, T. V. Raziman, Kilian D. Stenning, Will Branford, Kirsten Moselund, Heinz Schmid, Mauricio Barahona, Riccardo Sapienza
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Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC131181C (2024) https://doi.org/10.1117/12.3028385
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
A foundational component of vision processing is edge and feature detection. In the human eye, this is carried out powerfully via retinal ganglion cells which fight to suppress neuronal firing of neighbouring cells - a process termed 'lateral inhibition'. Such spatially-distributed activity competition leads to strong nonlinear enhancement of key image features such as edges, enabling more complex vision functionality including object recognition & motion detection. Software convolutional neural networks draw inspiration from this process and also begin with edge-detection, but in software this functionality is slow & the process intrinsically linear (matrix multiplications between the input image & convolutional kernels), with nonlinearity forced in via subsequent computationally-expensive activation functions such as ReLu. Here, we present a physical system which reproduces the strongly nonlinear lateral inhibition used in the retina. Using spatially-distributed mode-competition in nanoscale random network lasers, we demonstrate cutting-edge feature detection on complex images, and leverage this for a retinomorphic photonic convolutional neural network with strong performance.
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Jack C. Gartside, Wai Kit Ng, Anna Fischer, Jakub C. Dranczewski, Dhruv Saxena, Tobias Farchy, T. V. Raziman, Kilian D. Stenning, Will Branford, Kirsten Moselund, Heinz Schmid, Mauricio Barahona, and Riccardo Sapienza "Retinomorphic photonic machine vision via network lasers", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC131181C (4 October 2024); https://doi.org/10.1117/12.3028385
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KEYWORDS
Machine vision
Convolutional neural networks
Image enhancement
Image processing
Image processing software
Eye
Matrix multiplication
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