  In modern digital circuit back-end design, designers heavily rely on
electronic-design-automoation (EDA) tool to close timing. However, the
heuristic algorithms used in the place and route tool usually does not result
in optimal solution. Thus, significant design effort is used to tune parameters
or provide user constraints or guidelines to improve the tool performance. In
this paper, we targeted at those optimization space left behind by the EDA
tools and propose a machine learning framework that helps to define what are
the guidelines and constraints for registers placement, which can yield better
performance and quality for back-end design. In other words, the framework is
trying to learn what are the flaws of the existing EDA tools and tries to
optimize it by providing additional information. We discuss what is the proper
input feature vector to be extracted, and what is metric to be used for
reference output. We also develop a scheme to generate perturbed training
samples using existing design based on Gaussian randomization. By applying our
methodology, we are able to improve the design runtime by up to 36% and timing
quality by up to 23%.

Machine Learning