Advanced logic and physical synthesis tools provide numerous options and parameters that can drastically impact design quality; however, the large number of options leads to a complex design space difficult for human designers to navigate. By employing intelligent search strategies and parallel computing we can tackle this parameter tuning problem, thus automating one of the key design tasks conventionally performed by a human designer. To fully utilize the optimization potential of these tools, we propose SynTunSys, a system that adds a new level of abstraction between designers and design tools for managing the design space exploration process. SynTunSys takes control of the synthesis parameter tuning process, i.e., job submission, results analysis, and next-step decision making, automating one of the more difficult decision processes faced by designers. This system has been employed for optimizing multiple IBM high-performance server chips and presents numerous opportunities for future intelligent automation research.
CITATION STYLE
Ziegler, M. M., Liu, H. Y., Gristede, G., Owens, B., Nigaglioni, R., Kwon, J., & Carloni, L. P. (2019). SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors. In Machine Learning in VLSI Computer-Aided Design (pp. 539–570). Springer International Publishing. https://doi.org/10.1007/978-3-030-04666-8_18
Mendeley helps you to discover research relevant for your work.