Approximate Computing and the Efficient Machine Learning Expedition

TL;DR

This paper explores how approximate computing enhances the efficiency of machine learning systems by reducing computational costs while maintaining acceptable accuracy, highlighting its practical applications and benefits.

Contribution

It provides an overview and taxonomy of approximate computing in machine learning, demonstrating its impact through application scenarios and emphasizing its practical significance.

Findings

Approximate computing significantly improves ML system efficiency.

AxC reduces computational overheads in ML applications.

Practical scenarios show tangible efficiency gains.

Abstract

Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC research activities in various application domains, AxC thrived the past decade when it was applied in Machine Learning (ML). The by definition approximate notion of ML models but also the increased computational overheads associated with ML applications-that were effectively mitigated by corresponding approximations-led to a perfect matching and a fruitful synergy. AxC for AI/ML has transcended beyond academic prototypes. In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems. To that end, we present an overview and taxonomy of AxC for ML and use two descriptive application scenarios to demonstrate how AxC boosts the efficiency of ML systems.