1 research outputs found
CAD Tool Design Space Exploration via Bayesian Optimization
The design complexity is increasing as the technology node keeps scaling
down. As a result, the electronic design automation (EDA) tools also become
more and more complex. There are lots of parameters involved in EDA tools,
which results in a huge design space. What's worse, the runtime cost of the EDA
flow also goes up as the complexity increases, thus exhaustive exploration is
prohibitive for modern designs. Therefore, an efficient design space
exploration methodology is of great importance in advanced designs. In this
paper we target at an automatic flow for reducing manual tuning efforts to
achieve high quality circuits synthesis outcomes. It is based on Bayesian
optimization which is a promising technique for optimizing black-box functions
that are expensive to evaluate. Gaussian process regression is leveraged as the
surrogate model in Bayesian optimization framework. In this work, we use 64-bit
prefix adder design as a case study. We demonstrate that the Bayesian
optimization is efficient and effective for performing design space exploration
on EDA tool parameters, which has great potential for accelerating the design
flow in advanced technology nodes.Comment: 6 pages, 5 figure