Genetic-Algorithm-Based FPGA Architectural Exploration Using Analytical Models

Abstract

FPGA architectural optimization has emerged as one of the most important digital design challenges. In recent years, experimental methods have been replaced by analytical ones to find the optimized architecture. Time is the main reason for this replacement. Conventional Geometric Programming (GP) is a routine framework to solve analytical models, including area, delay, and power models. In this article, we discuss the application of the Genetic Algorithm (GA) to the design of FPGA architectures. The performance model has been integrated into the Genetic Algorithm framework in order to investigate the impact of various architectural parameters on the performance efficiency of FPGAs. This way, we are able to rapidly analyze FPGA architectures and select the best one. The main advantages of using GA versus GP are concurrency and speed. The results show that concurrent optimization of high-level architecture parameters, including lookup table size ( K ) and cluster size ( N ), and low-level parameters, like scaling of transistors, is possible for GA, whereas GP does not capture K and N under its concurrency and it needs to exhaustively search all possible combinations of K and N . The results also show that more than two orders of magnitude in runtime improvement in comparison with GP-based analysis is achieved.