A Fast On-Chip Adaptive Genetic Algorithm Processor for Evolutionary FIR Filter Implementation Using Hardware–Software Co-Design

Abstract

Recent studies show the impact of genetic algorithms (GA) in the design of evolutionary finite impulse response (FIR) filters. Studies have shown hardware and software method of GA implementation for design. Hardware method improves speed due to parallelism, pipelining and the absence of the function calls compared to software implementation. But area constraint was the main issue of hardware implementation. Therefore, this paper illustrates a hardware–software co-design concept to implement an Adaptive GA processor (AGAP) for FIR filter design. The architecture of AGAP uses adaptive crossover and mutation probabilities to speed up the convergence of the GA process. The AGAP architecture was implemented using Verilog Hardware Description Language (HDL) and instantiated as a custom intellectual property (IP) core to the soft-core MicroBlaze processor of Spartan 6 (XC6SLX45-3CSG324I) FPGA. The MicroBlaze processor controls the AGAP IP core and other interfaces using Embedded C programs. The experiment demonstrated a significant 134% improvement in speed over hardware implementation but with a marginal increase in area. The complete evaluation and evolution of the filter coefficients were executed on a single FPGA. The system on chip (SoC) concept enables a robust and flexible system.