Deep Learning Training with Simulated Approximate Multipliers

TL;DR

This paper explores how simulated approximate multipliers can accelerate CNN training by improving speed, power, and area efficiency, while proposing a hybrid method to maintain accuracy.

Contribution

It introduces a hybrid training approach that combines approximate and exact multipliers to optimize CNN training performance and accuracy.

Findings

Approximate multipliers significantly improve training speed, power, and area.

Using the hybrid method reduces accuracy loss during training.

The approach enables efficient CNN training with minimal accuracy compromise.

Abstract

This paper presents by simulation how approximate multipliers can be utilized to enhance the training performance of convolutional neural networks (CNNs). Approximate multipliers have significantly better performance in terms of speed, power, and area compared to exact multipliers. However, approximate multipliers have an inaccuracy which is defined in terms of the Mean Relative Error (MRE). To assess the applicability of approximate multipliers in enhancing CNN training performance, a simulation for the impact of approximate multipliers error on CNN training is presented. The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy. Additionally, the paper proposes a hybrid training method which mitigates this negative impact on the accuracy. Using the proposed hybrid method, the training can start using approximate multipliers then switches to exact multipliers for the last few epochs. Using this method, the performance benefits of approximate multipliers in terms of speed, power, and area can be attained for a large portion of the training stage. On the other hand, the negative impact on the accuracy is diminished by using the exact multipliers for the last epochs of training.