Elax: LLM-Guided Evolutionary Neural Architecture Search for Heterogeneous Edge Systems

Deploying neural networks across heterogeneous edge platforms is challenging because architecture accuracy-latency trade-offs vary across hardware, while independently training and profiling large numbers of candidate architectures is expensive. We present Elax, an LLM-assisted evolutionary neural architecture search framework that combines elastic weight sharing with hardware-aware multi-objective optimization. Elax converts a pretrained backbone into an elastic supernet and trains its shared weights using feature-level transfer and mixed random and LLM-guided subnet sampling. The trained weights are then frozen and reused during profile-specific evolutionary search. Rather than serving as a fitness predictor, the LLM proposes legal subnet configurations and local architectural mutations conditioned on the current architecture, observed performance, and hardware constraints. We evaluate Elax on elastic CNN and transformer backbones across CIFAR-10, CIFAR-100, and ImageNet-1K, and perform hardware-aware benchmark comparisons on NAS-Bench-201 and HW-NAS-Bench across multiple device profiles. The results show that Elax finds subnetworks with varying accuracy and latency trade-offs across devices and achieves competitive Pareto-front quality under matched architecture-evaluation budgets compared with adapted multi-objective search baselines.