Neural Architecture Search
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
An automated method that designs a neural network's structure, searching over the layers and connections that engineers would otherwise pick by hand.
What is Neural Architecture Search?
Neural architecture search (NAS) is an automated method that designs the structure of a neural network itself, searching over choices of layers, connections, and operations that engineers would otherwise pick by hand.
It is hyperparameter tuning taken to its logical end. Instead of tuning a few settings around a fixed design, the design becomes the thing being searched.
How Neural Architecture Search Works
A NAS system has three parts. The search space defines which architectures are possible: which layer types exist, how they connect, how deep the network goes. The search strategy decides which candidate to try next, using reinforcement learning, evolutionary algorithms, Bayesian methods, or gradient-based relaxations of the space.
The third part, performance estimation, is the bottleneck. Judging a candidate honestly means training it, and training thousands of candidates costs enormous compute. Practical NAS leans on shortcuts: smaller proxy tasks, early stopping, or weight sharing between candidates so each new one starts warm.
Example of Neural Architecture Search
Google’s NASNet project is the landmark case. A recurrent controller network proposed candidate architectures, each candidate was trained and scored on the small CIFAR-10 image dataset, and the controller was updated with reinforcement learning to propose better ones.
The best building block found on CIFAR-10 was then stacked into a larger network for ImageNet, where it matched or beat the leading hand-designed model architectures of its time. The search itself ran on hundreds of GPUs for days, which is why later NAS research focused as much on cutting search cost as on the architectures found.
NAS results now ship in production. Google’s EfficientNet and MobileNetV3 families, both widely used for image tasks on phones and other constrained hardware, came out of NAS pipelines rather than manual design.
Related AI terms: Hyperparameter Tuning · Hyperparameter · Model Architecture · Deep Learning · Neural Network
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About