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-b...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.展开更多
基金supported by the Science and Technology Project of the State Grid Zhejiang Electric Power Company Limited(No.B311XT21004G)。
文摘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.
