Automated parallel exploration of datapath and Unrolling Factor in High Level Synthesis using hyper-dimensional particle swarm encoding

Abstract

This paper presents a novel methodology for automated parallel exploration of datapath and Unrolling Factor (UF) in High Level Synthesis (HLS) using hyper-dimensional particle encoding based swarm intelligence (termed as ‘H-SI’) for control and data flow graphs (CDFGs). The major novel contributions of the proposed approach are as follows: (a) Hybrid design space exploration (DSE) framework that concurrently balances the tradeoff between the following: i) conflicting metrics of power - execution time ii) control states and execution delay during loop unrolling iii) orthogonal condition of enhancing Quality of Results (QoR) and reducing the exploration runtime; (b) automated parallel exploration of data path and loop UF through an integrated hyper-dimensional particle encoding approach using swarm intelligence; (c) power model for evaluation of design points during exploration; (d) model for estimation of execution delay of a loop unrolled CDFG (for any UF value) based on an available resource without necessity of tediously unrolling the entire CDFG in most cases. Results indicated an average improvement in QoR of >38 % and reduction in runtime of > 93 % compared to recent approaches.