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

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, Fashion-MNIST, and CelebA datasets demonstrates a 20% reduction in training time compared to conventional GANarchitectures, with competitive generation quality. To enable practical deployment, the architecturewas 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., 64X64 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.