Hardware Accelerators in Autonomous Driving
This is an incremental overview targeting researchers and practitioners in autonomous driving to improve decision-making performance.
The paper addresses the performance limitations of traditional processors for machine vision in autonomous vehicles by advocating for hardware accelerators to meet the demands of higher autonomy levels, though it does not present specific experimental results or concrete numbers.
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.