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Machine learning predictive modelling high-level synthesis design space exploration

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2 Author(s)
CARRION SCHAFER, B. ; Syst. IP Core Lab., NEC Corp., Kawasaki, Japan ; Wakabayashi, K.

A machine learning-based predictive model design space exploration (DSE) method for high-level synthesis (HLS) is presented. The method creates a predictive model for a training set until a given error threshold is reached and then continues with the exploration using the predictive model avoiding time-consuming synthesis and simulations of new configurations. Results show that the authors' method is on average 1.92 times faster than a genetic-algorithm DSE method generating comparable results, whereas it achieves better results when constraining the DSE runtime. When compared with a previously developed simulated annealer (SA)-based method, the proposed method is on average 2.09 faster, although again achieving comparable results.

Published in:

Computers & Digital Techniques, IET  (Volume:6 ,  Issue: 3 )