Automating Generation of Reconfigurable Sequential Monte Carlo Designs

Prof. Wayne Luk from Imperial College London visited our group in August, 2014

Abstract
This talk introduces an approach for automating efficient implementation of reconfigurable designs for Sequential Monte Carlo (SMC) applications. The proposed approach consists of a parametrisable SMC computation engine, and an open-source software template which enables efficient mapping of a variety of SMC designs to reconfigurable hardware. Design parameters that are critical to the performance and to the solution quality are tuned using a machine learning algorithm based on surrogate modelling. Experimental results for several case studies show that design performance can be substantially improved after parameter optimisation. The proposed design flow demonstrates its capability of producing reconfigurable implementations have significant improvement in speed and in energy efficiency over optimised CPU and GPU implementations.