This study introduces an optical neural network(ONN)-based autoencoder for efficient image processing,utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks.To address the challen...This study introduces an optical neural network(ONN)-based autoencoder for efficient image processing,utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks.To address the challenges in efficient decoding,we propose a method that optimizes output processing through scalar multiplications,enhancing performance in generating higher-dimensional outputs.By employing on-system iterative tuning,we mitigate hardware imperfections and noise,progressively improving image reconstruction accuracy to near-digital quality.Furthermore,our approach supports noise reduction and optical image generation,enabling models such as denoising autoencoders,variational autoencoders,and generative adversarial networks.Our results demonstrate that ONN-based systems have the potential to surpass the energy efficiency of traditional electronic systems,enabling real-time,low-power image processing in applications such as medical imaging,autonomous vehicles,and edge computing.展开更多
In recent years,the explosive development of artificial intelligence implementing by artificial neural networks(ANNs)creates inconceivable demands for computing hardware.However,conventional computing hardware based o...In recent years,the explosive development of artificial intelligence implementing by artificial neural networks(ANNs)creates inconceivable demands for computing hardware.However,conventional computing hardware based on electronic transistor and von Neumann architecture cannot satisfy such an inconceivable demand due to the unsustainability of Moore’s Law and the failure of Dennard’s scaling rules.Fortunately,analog optical computing offers an alternative way to release unprecedented computational capability to accelerate varies computing drained tasks.In this article,the challenges of the modern computing technologies and potential solutions are briefly explained in Chapter 1.In Chapter 2,the latest research progresses of analog optical computing are separated into three directions:vector/matrix manipulation,reservoir computing and photonic Ising machine.Each direction has been explicitly summarized and discussed.The last chapter explains the prospects and the new challenges of analog optical computing.展开更多
基金supported by the National Research Foundation(NRF)of Korea funded by the Ministry of Science,ICT and Future Planning(MSIP)of Korea(Nos.RS-2021-NR060087 and RS-2024-00353762)。
文摘This study introduces an optical neural network(ONN)-based autoencoder for efficient image processing,utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks.To address the challenges in efficient decoding,we propose a method that optimizes output processing through scalar multiplications,enhancing performance in generating higher-dimensional outputs.By employing on-system iterative tuning,we mitigate hardware imperfections and noise,progressively improving image reconstruction accuracy to near-digital quality.Furthermore,our approach supports noise reduction and optical image generation,enabling models such as denoising autoencoders,variational autoencoders,and generative adversarial networks.Our results demonstrate that ONN-based systems have the potential to surpass the energy efficiency of traditional electronic systems,enabling real-time,low-power image processing in applications such as medical imaging,autonomous vehicles,and edge computing.
文摘In recent years,the explosive development of artificial intelligence implementing by artificial neural networks(ANNs)creates inconceivable demands for computing hardware.However,conventional computing hardware based on electronic transistor and von Neumann architecture cannot satisfy such an inconceivable demand due to the unsustainability of Moore’s Law and the failure of Dennard’s scaling rules.Fortunately,analog optical computing offers an alternative way to release unprecedented computational capability to accelerate varies computing drained tasks.In this article,the challenges of the modern computing technologies and potential solutions are briefly explained in Chapter 1.In Chapter 2,the latest research progresses of analog optical computing are separated into three directions:vector/matrix manipulation,reservoir computing and photonic Ising machine.Each direction has been explicitly summarized and discussed.The last chapter explains the prospects and the new challenges of analog optical computing.
