Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks

TL;DR

This paper presents a post-training quantization method for deep neural networks that reduces energy consumption and memory footprint without retraining, achieving competitive accuracy on ImageNet.

Contribution

It introduces a systematic analysis of quantization options and demonstrates a fast, retraining-free quantization process that maintains high accuracy.

Findings

Achieves 2.2% higher Top-1 accuracy at 6-bit quantization on ImageNet.

Surpasses floating-point accuracy with 8-bit quantization without retraining.

Independent of network depth, applicable to uniform quantization.

Abstract

The biggest challenge for the deployment of Deep Neural Networks (DNNs) close to the generated data on edge devices is their size, i.e., memory footprint and computational complexity. Both are significantly reduced with quantization. With the resulting lower word-length, the energy efficiency of DNNs increases proportionally. However, lower word-length typically causes accuracy degradation. To counteract this effect, the quantized DNN is retrained. Unfortunately, training costs up to 5000x more energy than the inference of the quantized DNN. To address this issue, we propose a post-training quantization flow without the need for retraining. For this, we investigated different quantization options. Furthermore, our analysis systematically assesses the impact of reduced word-lengths of weights and activations revealing a clear trend for the choice of word-length. Both aspects have not been systematically investigated so far. Our results are independent of the depth of the DNNs and apply to uniform quantization, allowing fast quantization of a given pre-trained DNN. We excel state-of-the-art for 6 bit by 2.2% Top-1 accuracy for ImageNet. Without retraining, our quantization to 8 bit surpasses floating-point accuracy.