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.
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