Dataflow Accelerator Architecture for Autonomous Machine Computing
This addresses the problem of ad hoc and non-extensible computing solutions for companies in the autonomous machine sector, which is described as a thriving and potentially ubiquitous platform.
The paper tackles the lack of a suitable computing substrate for autonomous machines by proposing the Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle designed to match the characteristics of autonomous machine software.
Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software.