Rapid design space exploration using legacy design data and technology scaling trend

Rapid and effective design space exploration at all stages of a design process enables faster design convergence and shorter time-to-market. This is particularly important during the early stage of a design where design decisions can have a significant impact on design convergence. This paper describes a methodology for design space exploration using design target prediction models. These models are driven by legacy design data, technology scaling trends and, an in situ model-fitting process. Experiments on ISCAS benchmark circuits validate the feasibility of the proposed approach and yielded power centric designs that improved power by 7–32% for a corresponding 0–9% performance impact; or performance centric designs with improved performance of 10.31–17% for a corresponding 2–3.85% power penalty. Evolutionary algorithm based Pareto analysis on an industrial 65 nm design uncovered design tradeoffs which are not obvious to designers and optimize both power and performance. The high performance design option of the industrial design improved the straight-ported design's performance by 29% with a 2.5% power penalty, whereas the low power design option reduced the straight-ported design's power consumption by 40% for a 9% performance penalty.