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Machine learning predictive modelling high-level synthesis design space exploration

Machine learning predictive modelling high-level synthesis design space exploration

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© The Institution of Engineering and Technology
Published

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.

Inspec keywords: high level synthesis; learning (artificial intelligence)

Other keywords: design space exploration; predictive modelling; error threshold; genetic-algorithm DSE; high-level synthesis; machine learning; training set; simulated annealer

Subjects: Computer-aided circuit analysis and design; Optimisation techniques; Electronic engineering computing; Knowledge engineering techniques; Optimisation techniques

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