# What is Neural Architecture Search? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Operations  
> **Canonical Citation:** [https://spidits.com/ai-glossary/nas](https://spidits.com/ai-glossary/nas)

## Definition
Neural Architecture Search (NAS) is an automated process for designing artificial neural networks. By defining a search space, search strategy, and performance metric, NAS algorithms automatically discover optimal layer configurations.

## Architectural Mechanics & Deep Dive
Neural Architecture Search (NAS) is an automated process designed to find the optimal neural network architecture for a given task, replacing manual, trial-and-error network design. By defining a search space, a search strategy (such as reinforcement learning or evolutionary algorithms), and a performance estimation metric, NAS discovers highly efficient networks (like MobileNetV3) optimized for accuracy and hardware constraints.

## Industry Applications & Real-World Use Cases
Automated network layout, deep learning architecture optimization, and custom hardware target models

## Frequently Asked Questions

### Why is NAS valuable?
It eliminates the human trial-and-error process of designing layers, discovering architectures that outperform human-designed counterparts.

### What is the main drawback of NAS?
It is extremely resource-heavy, requiring training thousands of model variants to evaluate performance.

## Related Terminology
- [neural-network](https://spidits.com/ai-glossary/neural-network)
- [deep-learning](https://spidits.com/ai-glossary/deep-learning)
- [hyperparameter](https://spidits.com/ai-glossary/hyperparameter)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*