Hardware Accelerators in Autonomous Driving

TL;DR

This paper reviews hardware accelerators designed for machine vision in autonomous vehicles, emphasizing their role in enhancing performance and reliability for higher levels of autonomy.

Contribution

It provides an overview of ML accelerators in autonomous driving, with examples, recommendations, and future research directions.

Findings

Hardware accelerators improve processing speed and accuracy in autonomous vehicle perception.

Examples of accelerators demonstrate their effectiveness in machine vision tasks.

The paper highlights ongoing research needs in this emerging field.

Abstract

Computing platforms in autonomous vehicles record large amounts of data from many sensors, process the data through machine learning models, and make decisions to ensure the vehicle's safe operation. Fast, accurate, and reliable decision-making is critical. Traditional computer processors lack the power and flexibility needed for the perception and machine vision demands of advanced autonomous driving tasks. Hardware accelerators are special-purpose coprocessors that help autonomous vehicles meet performance requirements for higher levels of autonomy. This paper provides an overview of ML accelerators with examples of their use for machine vision in autonomous vehicles. We offer recommendations for researchers and practitioners and highlight a trajectory for ongoing and future research in this emerging field.