PowerQuant: Automorphism Search for Non-Uniform Quantization

TL;DR

PowerQuant introduces a data-free, non-uniform quantization method for deep neural networks by optimizing power functions, enabling improved accuracy without hardware changes or significant overhead.

Contribution

The paper proposes PowerQuant, a novel non-uniform quantization technique based on automorphism search, which enhances DNN compression without hardware modifications or data dependence.

Findings

Outperforms existing quantization methods across various configurations.

Requires only minor modifications to activation functions.

Achieves significant accuracy improvements with negligible overhead.

Abstract

Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of compression techniques such as quantization which consists in lowering the number of bits used to encode the weights and activations. Growing concerns for privacy and security have motivated the development of data-free techniques, at the expanse of accuracy. In this paper, we identity the uniformity of the quantization operator as a limitation of existing approaches, and propose a data-free non-uniform method. More specifically, we argue that to be readily usable without dedicated hardware and implementation, non-uniform quantization shall not change the nature of the mathematical operations performed by the DNN. This leads to search among the continuous automorphisms of $(\mathbb{R}_+^*,\times)$, which boils down to the power functions defined by their exponent. To find this parameter, we propose to optimize the reconstruction error of each layer: in particular, we show that this procedure is locally convex and admits a unique solution. At inference time, we show that our approach, dubbed PowerQuant, only require simple modifications in the quantized DNN activation functions. As such, with only negligible overhead, it significantly outperforms existing methods in a variety of configurations.