Fighting Quantization Bias With Bias

TL;DR

This paper identifies a bias-induced shift in activation means caused by quantization in low-precision neural networks, and proposes simple, fast correction methods to restore performance without extensive retraining.

Contribution

It introduces a bias compensation technique for quantized neural networks, effectively correcting activation shifts with minimal data and computation, improving deployment efficiency.

Findings

Bias correction restores network accuracy effectively.

Methods require only small unlabeled data sets.

Performance matches training-based quantization methods.

Abstract

Low-precision representation of deep neural networks (DNNs) is critical for efficient deployment of deep learning application on embedded platforms, however, converting the network to low precision degrades its performance. Crucially, networks that are designed for embedded applications usually suffer from increased degradation since they have less redundancy. This is most evident for the ubiquitous MobileNet architecture which requires a costly quantization-aware training cycle to achieve acceptable performance when quantized to 8-bits. In this paper, we trace the source of the degradation in MobileNets to a shift in the mean activation value. This shift is caused by an inherent bias in the quantization process which builds up across layers, shifting all network statistics away from the learned distribution. We show that this phenomenon happens in other architectures as well. We propose a simple remedy - compensating for the quantization induced shift by adding a constant to the additive bias term of each channel. We develop two simple methods for estimating the correction constants - one using iterative evaluation of the quantized network and one where the constants are set using a short training phase. Both methods are fast and require only a small amount of unlabeled data, making them appealing for rapid deployment of neural networks. Using the above methods we are able to match the performance of training-based quantization of MobileNets at a fraction of the cost.