  Parameter quantization in convolutional neural networks (CNNs) can help
generate efficient models with lower memory footprint and computational
complexity. But, homogeneous quantization can result in significant degradation
of CNN model accuracy. In contrast, heterogeneous quantization represents a
promising approach to realize compact, quantized models with higher inference
accuracies. In this paper, we propose HQNNA, a CNN accelerator based on
non-coherent silicon photonics that can accelerate both homogeneously quantized
and heterogeneously quantized CNN models. Our analyses show that HQNNA achieves
up to 73.8x better energy-per-bit and 159.5x better throughput-energy
efficiency than state-of-the-art photonic CNN accelerators.
