TOSCA: Towards Semi-Centralized Scheduling in Dataflow Architectures via Hardware-Software Co-Design

Dataflow architectures are regarded as promising hardware accelerators in multiple domains due to their efficiency and flexibility. Contemporary applications impose demands on the dynamic scheduling capability of dataflow architectures. Nevertheless, existing approaches face challenges in achieving efficient dynamic scheduling for dataflow architectures: centralized scheduling incurs heavy control overhead due to the limited local agility, whereas decentralized scheduling suffers from constrained planning visibility due to the lack of global awareness. In this paper, we propose TOSCA, a semi-centralized scheduling method for dataflow architectures through hardware-software co-design. Firstly, we introduce a semi-centralized dynamic scheduling execution model, featuring on-demand triggering, global sensing, global-aware planning, and low-latency remapping. Secondly, we present a dataflow microarchitecture designed to support this execution model, synergizing global awareness with local agility through close collaboration of a global-monitoring controller and semi-proactive local PEs. Finally, we propose an adaptive load balancing scheme to efficiently select appropriate PEs as scheduling targets, leveraging the hardware flexibility to enhance parallelism while reducing cost. Experimental results show that in a wide range of representative applications, TOSCA achieves up to 1.52 × performance improvement and 1.40 × energy efficiency improvement over state-of-the-art dataflow architectures.