Machine learning predictive modelling high-level synthesis design space exploration PROJECT TITLE :Machine Learning predictive modelling high-level synthesis design space explorationABSTRACT:A Machine Learning-based mostly predictive model design area exploration (DSE) method for high-level synthesis (HLS) is presented. The tactic creates a predictive model for a coaching set till a given error threshold is reached and then continues with the exploration using the predictive model avoiding time-consuming synthesis and simulations of latest configurations. Results show that the authors?? technique is on average 1.92 times faster than a genetic-algorithm DSE method generating comparable results, whereas it achieves higher results when constraining the DSE runtime. In comparison with a previously developed simulated annealer (SA)-based mostly method, the proposed methodology is on average two.09 faster, although again achieving comparable results. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Morphable hundred-core heterogeneous architecture for energy-aware computation Diagnostic test-pattern generation targeting open-segment defects and its diagnosis flow