Klotski: DNN Model Orchestration Framework for Dataflow Architecture Accelerators

Dataflow architecture accelerators are a new kind of scalable DNN accelerators. The availability of input operands of the instructions solely determines the execution of instructions. This paper proposes the Klotski framework to solve DNN model orchestration for dataflow architecture accelerators. First, a Bayesian optimization-based entropy-directed partition algorithm is proposed to transform a DNN model into mu ops. Second, a unified formal formulation for mu ops scheduling and mapping is presented. Third, a two-stage methodology is proposed to decouple the scheduling and mapping, making the solution feasible. Extensive results show that Klotski outperforms baselines in runtime by an average of 9.55% and 48.48%