Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the edge.Implementing such learning on digital hardware suffers from the well-known von-Neumann and scaling bottl...Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the edge.Implementing such learning on digital hardware suffers from the well-known von-Neumann and scaling bottlenecks.The optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising solution.However,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge learning.Here,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical reactions.We first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and denoising.We then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot dataset.The HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems.展开更多
文摘The liquid crystal television spatial light modulator (LCTVSLM) characterized is usable in optical processing applications,e.g.,optical pattern recognition,associative memory, optical computing,correlation detection and optical data processing systems.The array performance and real-time optical correlation applications are reviewed.
基金supported by the National Key R&D Program of China(Grant No.2018YFA0701500)Hong Kong Research Grant Council(Grant No.27206321,17205922)+5 种基金the National Natural Science Foundation of China(Grant Nos.62122004,61874138,61888102,61771176,and 62171173)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB44000000)Research on the GaN Chip for 5G Applications(Grant No:JCYJ20210324120409025)Research on high-reliable GaN power device and the related industrial power system(Grant No:HZQBKCZYZ-2021052)Key Project of Department of Education of Guangdong Province(No.2018KCXTD026)supported by ACCESS-AI Chip Center for Emerging Smart Systems,sponsored by Innovation and Technology Fund(ITF),Hong Kong SAR.
文摘Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the edge.Implementing such learning on digital hardware suffers from the well-known von-Neumann and scaling bottlenecks.The optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising solution.However,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge learning.Here,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical reactions.We first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and denoising.We then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot dataset.The HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems.
