Evolutionary-Enhanced GAN With Wavelet-Based Discrimination: A Hardware-Accelerated Architecture for Efficient Synthetic Image Generation

Abstract

This study presents a resource-efficient synthetic image generation architecture that integrates evolutionary optimization with wavelet-based adversarial learning. The proposed framework embeds a genetic algorithm within the generator to adaptively optimize network parameters, while the discriminator employs wavelet transform-based feature extraction to enhance classification fidelity and accelerate convergence. Experimental evaluation using the Fréchet Inception Distance (FID) across MNIST, FashionMNIST, and CelebA datasets demonstrates a 20% reduction in training time compared to conventional GAN architectures, with competitive generation quality. To enable practical deployment, the architecture was synthesized and implemented on the AMD Xilinx Kintex-7 FPGA KC705 platform. Benchmarking against state-of-the-art models reveals substantial computational gains: the proposed design achieves a 33%-76% reduction in inference latency (138µs vs. 570µs), a 36% decrease in power consumption (24mW vs. 47mW), and the lowest area footprint (21.7%) among all compared implementations. Resource utilization metrics further highlight architectural efficiency, requiring only 6,000 LUTs, 4,500 FFs, 10 BRAMs, and 6 DSPs on MNIST—representing a 43%-50% reduction relative to prior works. As dataset complexity increases, the model scales predictably, with deeper pipelines and expanded tiling (e.g., 64 × 64 for CelebA), while maintaining synthesis stability. The convergence of evolutionary search, wavelet-based discrimination, and hardware-aware design establishes a robust framework for real-time synthetic data generation in resource constrained environments. The proposed architecture offers a reproducible and modular foundation for edge AI applications, enabling efficient deployment across diverse domains