hardware-aware nas

**Hardware-Aware NAS** is a **neural architecture search approach that explicitly considers target hardware constraints** — incorporating latency, energy consumption, memory usage, and FLOPs directly into the search objective to find architectures that are Pareto-optimal for accuracy vs. efficiency. **How Does Hardware-Aware NAS Work?** - **Objective**: $min_alpha mathcal{L}_{CE}(alpha)$ subject to $Latency(alpha) leq T_{target}$ - **Latency Estimation**: Lookup tables (real hardware profiling), analytical models, or differentiable predictors. - **Hardware Targets**: GPU (NVIDIA), mobile CPU (ARM Cortex), NPU (Qualcomm), edge TPU (Google). - **Examples**: MNASNet, EfficientNet, ProxylessNAS, OFA. **Why It Matters** - **FLOPs ≠ Latency**: Two architectures with the same FLOPs can have very different real-world latency (memory access patterns, parallelism). - **Deployment-Ready**: Produces architectures ready for deployment on specific hardware — no further optimization needed. - **Industry Standard**: All major mobile/edge AI deployments use hardware-aware NAS architectures. **Hardware-Aware NAS** is **co-designing algorithms with silicon** — finding the neural network architecture that best exploits the specific capabilities of the target chip.