Designing state-of-the-art neural network architectures traditionally required months of human intuition and empirical trial-and-error. Neural Architecture Search (NAS) automates this discovery process using optimization algorithms.
By exploring continuous relaxation spaces (Differentiable Architecture Search / DARTS), systems search billions of potential layer, kernel, and attention-head permutations to construct architectures optimized simultaneously for top-1 accuracy, latency, and FLOP count on target hardware.
