Multi-Scale Hierarchical Diffusion Networks for Efficient Layout Generation: Improving Efficiency via Hierarchical Framework and Multi-Decoder Architectures - Kalyan Chakravarthy (Journal of Computer Networks, Architecture and High Performance Computing, 2026)
Layout generation remains a challenging task in automated design systems, where existing diffusion models often require extensive computational resources and numerous sampling steps. This work presents a novel multi-scale hierarchical diffusion architecture that achieves state-of-the-art performance through explicit three-level processing with progressive dimensional reduction (128d ? 64d ? 32d). The proposed framework demonstrates 92.5% loss reduction (0.496 to 0.037) over 50 training epochs with only 21,862 parameters, representing a 2.1× reduction compared to existing diffusion-based methods while maintaining superior generation quality. Experimental validation demonstrates the efficiency benefits of hierarchical design across multiple metrics including FID scores (12.3 vs 18.7), precision (0.87 vs 0.79), and training time (0.049s vs 0.127s per epoch). Comprehensive ablation studies quantify the contribution of each hierarchical level and validate architectural design choices.