The Promise of Dataflow Architectures in the Design of Processing Systems for Autonomous Machines

The commercialization of autonomous machines is a thriving sector, and likely to be the next major computing demand driver, after PC, cloud computing, and mobile computing. Nevertheless, a suitable computer architecture for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither scalable nor extensible. In this article, we analyze the demands of autonomous machine computing, and argue for the promise of dataflow architectures in autonomous machines. The commercialization of autonomous machines is a thriving sector, with projected average compound annual growth rate (CAGR) of 26%, and by 2030 this sector will have a market size of $1 trillion [1]. Hence, this sector is likely to be the next major computing demand driver, after personal computers, cloud computing, and mobile computing. Autonomous machines exist in multiple forms, e.g., cars, aerial drones, service robots, industrial robots.