Rapid early design space exploration using legacy design data, technology scaling trend and in-situ macro models

This work proposes a system level design framework for early design space exploration with a focus on power and performance tradeoffs using analytical power and performance prediction models. The analytical prediction models are driven by legacy design data, technology scaling trend, low level physical design parameters and in-situ simulations. 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 11.25%--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.;The design framework and methodology developed and demonstrated in this work form the foundational steps for early design space exploration utilizing technology scaling trends, process dependent parameters and in-situ simulations. Analytical prediction models are currently limited only to predicting power and performance. Prediction models for yield, chip area and system reliability are seen as valuable future additions to EIDAs capability. Modeling the impact of process variation and the ability to incorporate statistical inputs and outputs are seen as an another incremental improvement to EIDAs value as a design tool. In addition to the above improvements, a macromodel based critical path delay calculation technique including clock and signal uncertainties, incorporating special libraries, RF and analog modules in the system model and, improving the evolutionary algorithm used for design space exploration are salient direction for future research. (Abstract shortened by UMI.)...