We present an overview of techniques for quantizing convolutional neural networks for inference with integer weights and activations. Per-channel quantization of weights and per-layer quantization of activations to 8-bits of precision post-training produces classification accuracies within 2% of floating point networks for a wide variety of CNN architectures. Model sizes can be reduced by a factor of 4 by quantizing weights to 8-bits, even when 8-bit arithmetic is not supported. This can be achieved with simple, post training quantization of weights.We benchmark latencies of quantized networks on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations compared to floating point on CPUs. Speedups of up to 10x are observed on specialized processors with fixed point SIMD capabilities, like the Qualcomm QDSPs with HVX. Quantization-aware training can provide further improvements, reducing the gap to floating point to 1% at 8-bit precision. Quantization-aware training also allows for reducing the precision of weights to four bits with accuracy losses ranging from 2% to 10%, with higher accuracy drop for smaller networks.We introduce tools in TensorFlow and TensorFlowLite for quantizing convolutional networks and review best practices for quantization-aware training to obtain high accuracy with quantized weights and activations. We recommend that per-channel quantization of weights and per-layer quantization of activations be the preferred quantization scheme for hardware acceleration and kernel optimization. We also propose that future processors and hardware accelerators for optimized inference support precisions of 4, 8 and 16 bits.

本文概述了卷积神经网络量化技术，研究发现通过对权重和激活进行逐通道和逐层量化，即使在不支持8位运算的情况下，将权重量化为8位可以将模型大小降低4倍，并且分类的准确率可以达到浮点型卷积神经网络的98%。作者介绍了针对CPU和DSP的量化网络的等待时间基准测试，并观察到相比于CPU上的浮点运算，量化实现的速度提高了2倍至3倍。作者提出了一种通过TensorFlow和TensorFlowLite进行卷积网络量化的工具，并回顾了用于量化训练的最佳实践。作者建议，对于硬件加速和内核优化，应将逐通道量化的权重和逐层量化的激活作为首选量化方案，并提议未来处理器和硬件加速器用于优化推断时支持4、8和16位的精度。

深度卷积网络的量化优化：一篇白皮书