MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Filtering

We present MARCO (Multi-Agent Reinforcement learning with Conformal Optimization), a hardware-aware neural architecture search (NAS) framework for resource-constrained edge devices. MARCO combines multi-agent reinforcement learning (MARL) with Conformal Prediction (CP) to efficiently explore architectures under strict memory and latency budgets. Unlike once-for-all (OFA) supernets that require expensive pretraining, MARCO separates the NAS task into a Hardware Configuration Agent and a Quantization Agent, coordinated via a centralized-critic, decentralized-execution (CTDE) paradigm. A calibrated CP surrogate model offers distribution-free guarantees to filter low-reward candidates before costly training or simulation, significantly accelerating the search. Experiments on MNIST, CIFAR-10, and CIFAR-100 show MARCO achieves $3-4 \times$ faster search than OFA while maintaining accuracy within 0.3% and reducing latency. Validation on the MAX78000 confirms simulator fidelity with less than 5% error.