neural architecture search for edge

**NAS for Edge** (Neural Architecture Search for Edge) is the **automated design of neural network architectures that meet strict edge deployment constraints** — searching for architectures that maximize accuracy while staying within target latency, memory, FLOPs, and power budgets. **Edge-Aware NAS Methods** - **MnasNet**: Multi-objective search optimizing accuracy × latency on target mobile hardware. - **FBNet**: DNAS (differentiable NAS) with hardware-aware latency lookup tables. - **ProxylessNAS**: Search directly on target hardware (no proxy tasks) — real latency feedback. - **Once-for-All**: Train one super-network, then extract specialized sub-networks for different hardware targets. **Why It Matters** - **Hardware-Specific**: Models designed for specific edge hardware (Cortex-M, Jetson, iPhone) outperform generic architectures. - **Automated**: Removes the need for manual architecture engineering — the search finds optimal designs. - **Multi-Objective**: Simultaneously optimizes accuracy, latency, memory, and energy — impossible to do manually. **NAS for Edge** is **automated architect for tiny devices** — using search algorithms to find the best neural network architecture for specific edge hardware constraints.