DFGC: DFG-aware NoC Control based on Time Stamp Prediction for Dataflow Architecture
Tianyu Liu, Wenming Li, Zhihua Fan · 2023
Coarse-grained reconfigurable architectures (CGRAs) have been regarded as promising accelerating paradigm for the ever-evolving algorithms in multi domains. Obtaining high energy-efficiency on CGRAs relies heavily on the combination of mapping, timing (issuing) and routing to decrease run-time idle. Statically configuration-driven designs are widely adopted, but the leak of hardware flexibility leads to a heavy reliance on burdensome scheduling of compiler to avoid over-serialization. This draw a trade-off between hardware/software co-design in CGRA scheduling.Unlike a static-schedule oriented approach, we propose DFGC (DFG-aware NoC Control), a dataflow-driven CGRA which takes the advantages of fully exploring data parallelism in different kernels by using dataflow dynamic firing with low overhead. The DFGC compiler is responsible for analyzing critical data paths and generating TimeStamp predictions instead of per cycle configuration. The rough predicted results enable router and PE to be sensitive to the entire dataflow graph, thereby accelerating the whole computation process. The DFG-aware NoC design realize a combined scheduling technique of hardware dynamic decision-making together with static prediction. DFGC represents a paradigm of CGRA that is worth exploring, achieving hardware/software co-design without relying on sophisticated designed compiler. Experiments show that DFGC achieves 1.32× energy efficiency improvement over a dataflow architecture and 1.8× energy efficiency improvement over a state-of-the-art static configured CGRA.