论文标题
具有异质量化的卷积神经网络的硅光子加速器
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization
论文作者
论文摘要
卷积神经网络（CNN）中的参数量化可以帮助生成具有较低记忆足迹和计算复杂性的有效模型。但是，均匀量化会导致CNN模型准确性的显着降解。相比之下，异质量化代表了一种有前途的方法，可以实现具有更高推理精度的紧凑，量化模型。在本文中，我们提出了基于非固体硅光子学的CNN加速器HQNNA，可以加速同质量化和异质量化的CNN模型。我们的分析表明，与最先进的光子CNN加速器相比，HQNNA的每位能量能量高达73.8倍，吞吐能量效率优于159.5倍。
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.
