Positive/Negative Approximate Multipliers for DNN Accelerators

TL;DR

This paper introduces a configurable approximate multiplier for DNN accelerators that balances energy savings and accuracy loss by dynamically selecting operation modes and mapping weights accordingly, achieving significant energy reduction.

Contribution

The work presents a novel dynamically configurable approximate multiplier supporting three modes and a filter-oriented mapping algorithm to optimize energy efficiency in DNNs.

Findings

Achieves 18.33% average energy savings across multiple DNNs.

Maintains a maximum of 1% accuracy drop.

Outperforms state-of-the-art approximation methods.

Abstract

Recent Deep Neural Networks (DNNs) managed to deliver superhuman accuracy levels on many AI tasks. Several applications rely more and more on DNNs to deliver sophisticated services and DNN accelerators are becoming integral components of modern systems-on-chips. DNNs perform millions of arithmetic operations per inference and DNN accelerators integrate thousands of multiply-accumulate units leading to increased energy requirements. Approximate computing principles are employed to significantly lower the energy consumption of DNN accelerators at the cost of some accuracy loss. Nevertheless, recent research demonstrated that complex DNNs are increasingly sensitive to approximation. Hence, the obtained energy savings are often limited when targeting tight accuracy constraints. In this work, we present a dynamically configurable approximate multiplier that supports three operation modes, i.e., exact, positive error, and negative error. In addition, we propose a filter-oriented approximation method to map the weights to the appropriate modes of the approximate multiplier. Our mapping algorithm balances the positive with the negative errors due to the approximate multiplications, aiming at maximizing the energy reduction while minimizing the overall convolution error. We evaluate our approach on multiple DNNs and datasets against state-of-the-art approaches, where our method achieves 18.33% energy gains on average across 7 NNs on 4 different datasets for a maximum accuracy drop of only 1%.