Evolutionary High Level Synthesis with Predictive Models
نویسنده
چکیده
In modern day chip design, the pressure for time to market is increasing and recovery time for investment is decreasing. So design times have to be shortened. An Artificial Neural Network (ANN) based predictive model for power to speed-up the cost function evaluation during High Level Synthesis (HLS) is presented here. Genetic Algorithms (GA) have been used successfully to solve HLS problems. Our proposed predictive model can be easily integrated with this evolutionary framework. The accuracy of this predictive model is tested with DFG benchmark circuits.
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تاریخ انتشار 2014