Highly efficient convolution computing architecture based on silicon photonic Fano resonance devices

查看全文 作　　者：NI [1]Jiarong;LU [2]Wenda;LAI [2]Xiaohan;LU [1]Lidan;OU [1]Jianzhen;ZHU [1]Lianqing

机构地区：[1]Beijing Information Science and Technology University,Beijing,100192,China;[2]State Grid Zhejiang Electric Power Corporation Information&Telecommunication Branch,Hangzhou,310007,China

出　　处：《Optoelectronics Letters》索引2023年第19卷第11期,共7页

基　　金：supported by the Science and Technology Project of the State Grid Zhejiang Electric Power Company Limited(No.B311XT21004G)。 摘　　要：Convolutional neural networks(CNNs)require a lot of multiplication and addition operations completed by traditional electrical multipliers,leading to high power consumption and limited speed.Here,a silicon waveguide-based wavelength division multiplexing(WDM)architecture for CNN is optimized with high energy efficiency Fano resonator.Coupling of T-waveguide and micro-ring resonator generates Fano resonance with small half-width,which can significantly reduce the modulator power consumption.Insulator dataset from state grid is used to test Fano resonance modulator-based CNNs.The results show that accuracy for insulator defect recognition reaches 99.27%with much lower power consumption.Obviously,our optimized photonic integration architecture for CNNs has broad potential for the artificial intelligence hardware platform. 关键词：RESONANCE CONVOLUTION HIGHLY

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Highly efficient convolution computing architecture based on silicon photonic Fano resonance devices

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