PreDAC: An Efficient Framework of Pre-Refining Enhanced Design Space Exploration for Approximate Computing

Approximate computing has emerged as a promising solution in energy-efficiency applications. Recently, attention has shifted from approximate components to Design Space Exploration (DSE) algorithms. However, traditional DSE algorithms face challenges in efficiently obtaining optimal solutions within large and complex design spaces. This paper introduces a prerefining enhanced design space exploration framework that provides customized design space and cost-performance formula for applications. Experimental results demonstrate that integrating this pre-refining step into various DSE algorithms leads to substantial performance gains, including up to $87 \times$ speedup and a 23% improvement in hardware overhead. Moreover, the innovative cost-performance-based DSE algorithm attains a $7.7 \times$ acceleration and further optimizes hardware metrics by an additional 8.8% compared to advanced frameworks employing the same pre-refinement.