  Optical neural networks (ONNs) have gained significant attention due to their
potential for high-speed and energy-efficient computation in artificial
intelligence. The implementation of optical convolutions plays a vital role in
ONNs, as they are fundamental operations within neural network architectures.
However, state-of-the-art convolution architectures often suffer from redundant
inputs, leading to substantial resource waste. Here, we propose an integrated
optical convolution architecture that leverages the inherent routing principles
of arrayed waveguide grating (AWG) to execute the sliding of convolution kernel
and summation of results. M*N multiply-accumulate (MAC) operations are
facilitated by M+N units within a single clock cycle, thus eliminating the
redundancy. In the experiment, we achieved 5-bit precision and 91.9% accuracy
in the handwritten digit recognition task confirming the reliability of our
approach. Its redundancy-free architecture, low power consumption, high compute
density (8.53 teraOP mm^-2 s^-1) and scalability make it a valuable
contribution to the field of optical neural networks, thereby paving the way
for future advancements in high-performance computing and artificial
intelligence applications.
