Hardware Aware Neural Architecture Search

Hardware-aware neural architecture search (HW-NAS) automates the design of neural networks optimized for specific hardware constraints, prioritizing efficiency alongside accuracy. Current research focuses on developing efficient algorithms, such as evolutionary and differentiable approaches, to explore diverse search spaces and incorporate various hardware metrics (latency, memory, energy consumption) into the optimization process, often employing models like MobileNet and Transformers as baselines or within hybrid architectures. This field is significant because it enables the deployment of powerful deep learning models on resource-constrained devices like microcontrollers and edge computers, impacting applications ranging from TinyML to autonomous driving.