Automated exploration of datapath and unrolling factor during power–performance tradeoff in architectural synthesis using multi-dimensional PSO algorithm Anirban Sengupta ⇑ , Vipul Kumar Mishra Discipline of Computer Science and Engineering, Indian Institute of Technology, Indore, India article info Keywords: Unrolling factor Multi-dimensional Particle encoding Power Execution time abstract A novel algorithm for automated simultaneous exploration of datapath and Unrolling Factor (UF) during power–performance tradeoff in High Level Synthesis (HLS) using multi-dimensional particle swarm optimization (PSO) (termed as ‘M-PSO’) for control and data flow graphs (CDFGs) is presented. The major contributions of the proposed algorithm are as follows: (a) simultaneous exploration of datapath and loop UF through an integrated multi-dimensional particle encoding process using swarm intelligence; (b) an estimation model for computation of execution delay of a loop unrolled CDFG (based on a resource configuration visited) without requiring to tediously unroll the entire CDFG for the specified loop value in most cases; (c) balancing the tradeoff between power–performance metrics as well as control states and execution delay during loop unrolling; (d) sensitivity analysis of PSO parameter such as swarm size on the impact of exploration time and Quality of Results (QoR) of the proposed design space exploration (DSE) process. This analysis presented would assist the designer in pre-tuning the PSO parameters to an optimum value for achieving efficient exploration results within a quick runtime; (e) analysis of design metrics such as power, execution time and number of control steps of the global best particle found in every iteration with respect to increase/decrease in unrolling factor. The proposed approach when tested on a variety of data flow graphs (DFGs) and CDFGs indicated an average improvement in QoR of >28% and reduction in runtime of >94% compared to recent works. Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction When digital systems are built, designers undergo numerous decision making steps at various levels of design abstraction (register transfer level, system/high level etc.) such as the type of architectural framework (datapath) required, exploring the best possible implementation alternative and managing hardware– software tradeoff. More formally, the above process is termed as design space exploration. Design space exploration when performed during high level synthesis becomes a non-trivial task as it involves multiple convoluted design decisions specially when simultaneously dealing with conflicting parameters such as power, area and performance. The above process becomes further intricate when an auxiliary variable called ‘loop unrolling factor’ joins the decision making process. Owing to the reasons above, architecture exploration suffers from exponential order of complexity with the increase in number of alternative solutions, thereby making it impossible to perform an exhaustive search (Coussy, Gajski, Takach, & Meredith, 2009; Coussy & Morawiec, 2008; De Micheli, 1994; Gajski, Dutt, Wu, & Lin, 1992; Mohanty, Ranganathan, Kougianos, & Patra, 2008; Zhang & Ng, 2000). Other DSE approaches in HLS employed very recently such as genetic algorithm (GA) (Sengupta, Sedaghat, and Sarkar (2012), Harish Ram, Bhuvaneswari, & Prabhu, 2012; Krishnan & Katkoori, 2006; Gallagher, Vigraham, & Kramer, 2004; Mandal, Chakrabarti, & Ghose, 2000) used for solving similar problem (but of lesser com- plexity as it did not include exploration of unrolling factor as well as tradeoff between power and execution time) have not been found suitable candidates owing to the computationally expensive runtime (growing exponentially) and lower guarantee of reaching optimal result. This has also been found after comparison with Krishnan and Katkoori (2006) and Sengupta, Sedaghat, and Sarkar (2012) where proposed PSO based approach produces better QoR with lesser exploration run time. Moreover, we have attained real optimal solutions (by comparing with golden solutions) for almost all cases unlike other heuristic approaches. Therefore, in order to combat the problem of exploration, a novel framework using PSO http://dx.doi.org/10.1016/j.eswa.2014.01.041 0957-4174/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +91 7324240735. E-mail address: asengupt@iiti.ac.in (A. Sengupta). Expert Systems with Applications 41 (2014) 4691–4703 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa