Neural Architecture Search for Edge Deployment: Latency-Aware Optimization
Deploying deep neural networks on edge devices demands architectures that balance accuracy with stringent latency, memory, and energy constraints. Conventional Neural Architecture Search (NAS) methods optimize primarily for accuracy on GPU clusters, producing architectures that are impractical for resource-constrained deployment. We introduce EdgeNAS, a latency-aware NAS framework that incorporates hardware-specific cost models directly into the search objective. EdgeNAS employs a differentiable search strategy over a mobile-optimized search space, using a multi-objective reward signal that jointly optimizes classification accuracy and measured on-device latency. We construct device-specific latency lookup tables for ARM Cortex-M and RISC-V microcontrollers, enabling accurate cost estimation without requiring physical hardware during search. On the Visual Wake Words benchmark, EdgeNAS discovers architectures achieving 89.3% accuracy at 12ms inference latency on Cortex-M7, outperforming MobileNetV3-Small (87.1% at 18ms) and MCUNet (88.5% at 15ms). Our framework reduces NAS compute cost by 83% compared to hardware-in-the-loop approaches while producing Pareto-superior architectures across four edge platforms.