OPLE: A Heuristic Custom Instruction Selection Algorithm Based on Partitioning and Local Exploration of Application Dataflow Graphs

In this article, a heuristic custom instruction (CI) selection algorithm is presented. The proposed algorithm, which is called OPLE for "Optimization based on Partitioning and Local Exploration," uses a combination of greedy and optimal optimization methods. It searches for the near-optimal solution by reducing the search space based on partitioning the identified CI set. The partitioning of the identified set guarantees the success of the algorithm independent of the size of the identified set. First, the algorithm finds the near-optimal CIs from the candidate CIs for each part. Next, the suggested CIs from different parts are combined to determine the final selected CI set. To improve the set of the selected CIs, the solution is evolved by calling the algorithm iteratively. The efficacy of the algorithm is assessed by comparing its performance to those of optimal and nonoptimal methods. A comparative study is performed for a number of benchmarks under different area budgets and I/O constraints. The results reveal higher speedups for the OPLE algorithm, especially for larger identified candidate sets and/or small area budgets compared to those of the nonoptimal solutions. Compared to the nonoptimal techniques, the proposed algorithm provides 30% higher speedup improvement on average. The maximum improvement is 117%. The results also demonstrate that in many cases OPLE is able to find the optimal solution.