Bio-Inspired Algorithms for Energy-Optimized FPGA Routeing

Abstract

Field-Programmable Gate Arrays (FPGAs) offer a flexible and reconfigurable platform for digital system design, widely adopted in applications ranging from telecommunications to edge computing and artificial intelligence. However, one of the critical challenges in FPGA design is the energy consumption associated with routing, which constitutes a significant portion of total power usage, particularly in high-density, large-scale designs. Traditional routing algorithms prioritize timing closure and resource utilization, often at the expense of energy efficiency. This paper proposes a novel methodology employing bio-inspired optimization algorithms—specifically Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA)—to minimize energy consumption during the FPGA routing phase. The proposed approach models the FPGA routing problem as a constrained optimization space where the objective is to reduce the switching activity, interconnect capacitance, and dynamic power without compromising timing and logic correctness. Each bio-inspired algorithm is adapted to consider energy-aware cost functions and routing heuristics derived from low-power design principles. We implement these algorithms within a custom FPGA routing framework compatible with standard design flows and benchmark them using the MCNC and VTR design suites.