  Photonic technologies have shown a promising way to build high-speed and
high-energy-efficiency neural network accelerators. In previously presented
photonic neural networks, architectures are mainly designed for fully-connected
layers. When convolutional layers are executed in such neural networks, the
large-scale electrooptic modulation array heavily increases the energy
dissipation on chip. To increase the energy efficiency, here we show an
integrated photonic architecture specifically for convolutional layer
calculations. Optical delay lines replace electronics to execute data
manipulations on optical chip, reducing the scale of electro-optic modulation
array. Consequently, the energy dissipation of these parts is mitigated.
Powered by wavelength division multiplexing, the footprint of delay lines is
significantly reduced compared with previous art, thus being practical to
fabricate. We evaluate the potential performance of the proposed architecture
with respect to component flaws in practical fabrications. According to the
results, with well-controlled system insertion loss, energy efficiency of the
proposed architecture would surpass previously presented works and the
state-of-art electronic processors. We anticipate the proposed architecture is
beneficial for future fast and energy-efficient convolutional neural network
accelerators.
