Approximate Computing for ML: State-of-the-art, Challenges and Visions

In this paper, we present our state-of-the-art approximate techniques that cover the main pillars of approximate computing research. Our analysis considers both static and reconfigurable approximation techniques as well as operation-specific approximate components (e.g., multipliers) and generalized approximate high-level synthesis approaches. As our application target, we discuss the improvements that such techniques bring on machine learning and neural networks. In addition to the conventionally analyzed performance and energy gains, we also evaluate the improvements that approximate computing brings in the operating temperature.

Automated summary

This paper presents state-of-the-art approximate computing techniques, covering static and reconfigurable approaches, operation-specific components, and generalized high-level synthesis methods. The focus is on the impact of these techniques on machine learning and neural networks, with evaluations not only on performance and energy gains but also on improvements in operating temperature.