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NAS and Automated Neural Network Architecture Search: The Future of AI Model Design

NAS و جستجوی خودکار معماری شبکه‌های عصبی: آینده طراحی مدل‌های هوش مصنوعی

Introduction

Imagine you want to build a complex structure, but instead of hiring an architect, you use an intelligent system that finds the best design itself. This is exactly what Neural Architecture Search (NAS) does in the world of deep learning. For years, designing neural network architectures was a manual, time-consuming, and experience-dependent process. Researchers had to spend countless hours on trial and error to reach a suitable architecture. But NAS changed the game.
NAS is a revolutionary approach in the AutoML domain that automatically discovers optimal architectures for neural networks. This technology not only reduces development time and cost but often achieves architectures that perform better than human-designed ones. With the growing applications of artificial intelligence, understanding NAS has become essential for anyone working in machine learning.

What is NAS? The Fundamental Concept

Neural Architecture Search is an automated process for finding the best neural network architecture for a specific task. Instead of humans manually determining the number of layers, types of layers, connections, and other parameters, NAS does this systematically and data-driven.
The NAS process is like a complex optimization game. The system explores a vast search space of possible architectures, evaluates each architecture, and tries to find the best option. This process typically involves millions of possible combinations, which would be impossible to perform manually.

Differences Between NAS and Traditional Design

In the traditional approach, a deep learning researcher designs the architecture based on intuition, experience, and trial and error. This process:
  • Depends on expertise and requires deep knowledge
  • Is time-consuming and may take weeks
  • Is limited to individual experience and may miss better solutions
  • Is not scalable for new problems
NAS eliminates these limitations by automating the design process and can explore the architecture space more systematically.

Main Components of NAS: Three Fundamental Pillars

Every NAS system consists of three main components, each playing a vital role in the search process:

1. Search Space

The search space is the set of all possible architectures that NAS can explore. This space determines what types of architectures can be built. Search spaces are divided into several categories:
Chain-structured Search Space: The simplest type where layers are placed sequentially one after another. Like classic CNN architectures where each layer connects to the next.
Cell-based Search Space: A more modern approach where a small "cell" is designed and then repeated multiple times. This method was popularized by Google Brain and used in architectures like NASNet.
Graph-based Search Space: The most flexible type where more complex connections between layers are possible, similar to Transformer architectures.
Search space design is one of the most important decisions in NAS. A larger space has more flexibility but makes the search harder, while a smaller space makes the search faster but may miss better solutions.

2. Search Strategy

The search strategy determines how to explore the search space. Several main approaches exist:
Reinforcement Learning: The first successful NAS methods used reinforcement learning. In this method, a controller (usually an RNN) proposes architectures and receives rewards based on their performance.
Evolutionary Methods: Genetic and evolutionary algorithms maintain a population of architectures and generate better generations through mutation, combination, and selection.
Bayesian Optimization: Model-based methods that try to build a probabilistic model of the search space and use it to guide the search.
Gradient-based Search: Newer approaches like DARTS that convert the discrete search space into a continuous space and use gradients for optimization.
Random Search: Sometimes used as a baseline and sometimes produces surprising results.

3. Performance Estimation Strategy

Fully training each architecture to evaluate it is very expensive. A NAS project may need to evaluate thousands of architectures. Therefore, more efficient strategies for estimating performance have been developed:
Training with Less Data: Using a subset of training data for faster evaluation.
Training for Fewer Epochs: Stopping training earlier than usual.
Learning Curve Extrapolation: Predicting final performance based on initial performance.
Weight Sharing: The SuperNet approach where all possible architectures share a set of weights. This method, used in ENAS and One-Shot NAS, dramatically reduces computational cost.
Proxy Tasks: Using simpler tasks for initial evaluation.

Prominent NAS Algorithms

NASNet and Reinforcement Learning Approach

NASNet, introduced by Google Brain in 2017, was one of the first major successes of NAS. This system used reinforcement learning to design convolutional cells and achieved better performance than manual architectures on the ImageNet dataset.
The architecture found by NASNet showed that:
  • NAS can build transferable architectures that perform well on different tasks
  • More complex connections than simple chains can be beneficial
  • But it has high computational cost (48,000 GPU hours)

ENAS: Cost Reduction with Weight Sharing

Efficient Neural Architecture Search (ENAS) was a turning point in reducing NAS costs. By introducing the concept of weight sharing, ENAS reduced search time from thousands of GPU hours to just a few hours.
In ENAS, all child architectures are considered as subgraphs of a large supergraph. Therefore, weights are shared between different architectures, eliminating the need for full training of each architecture.

DARTS: Gradient-based Search

Differentiable Architecture Search (DARTS) introduced a completely different approach. Instead of discrete search in the architecture space, DARTS converts the search space into a continuous space that enables gradient-based optimization.
In DARTS:
  • All possible operations are computed in parallel
  • Architecture weights are learned continuously
  • Finally, operations with the highest weights are selected
This method is very fast (usually less than a day on one GPU) and produces competitive results.

EfficientNet and Compound Scaling

EfficientNet took a different approach: instead of full architecture search, it focused on compound scaling. This architecture showed that balanced scaling of depth, width, and resolution leads to better results than one-dimensional scaling.
EfficientNet used NAS to find the base architecture and then scaled it with a compound coefficient. This method became one of the most popular architectures for computer vision.

Once-for-All Network (OFA)

OFA demonstrated that a single network can be trained to support thousands of subnetworks with different sizes and capabilities. This means training once and deploying to different devices with different constraints.
This approach is very important for Edge AI and mobile devices, where computational resources are limited.

Practical Applications of NAS

Computer Vision

NAS has had the most success in computer vision:
Image Classification: Architectures found by NAS like EfficientNet have excellent performance in classifying images on large datasets like ImageNet.
Object Detection: NAS has been used to design better backbones for object detection models like YOLO and Faster R-CNN.
Semantic Segmentation: NAS architectures are used for segmentation tasks in medicine, autonomous vehicles, etc.
Image Generation: In AI image generation systems like GANs and Diffusion models, NAS is used to find better architectures.

Natural Language Processing

Although Transformer is the dominant architecture in NLP, NAS still has important applications:
Transformer Optimization: Searching for the optimal number of heads, layers, and embedding sizes.
Small Language Models: Designing small language models for mobile devices.
Hybrid Architectures: Combining Transformer with other architectures like LSTM or GRU.

Recommender Systems

NAS is also used in designing neural networks for recommender systems, where the architecture must work with sparse data and diverse features.

Medicine and Medical Imaging

In medical diagnosis and treatment, NAS is used to design specialized architectures for:
  • Diagnosing diseases from radiological images
  • Genomic analysis
  • Predicting treatment response

AutoML and Democratizing ML

NAS is an important part of AutoML platforms that allow non-expert developers to build powerful models. Platforms like Google Cloud AI and Azure AutoML use NAS.

Challenges and Limitations of NAS

Computational Cost

Despite significant progress, NAS is still computationally expensive:
Need for Powerful GPUs: Many NAS algorithms require multiple GPUs or even TPUs.
Long Search Time: Even efficient methods may take hours or days.
Environmental Impact: The energy cost of NAS has raised concerns about environmental sustainability.
Limited Access: Small organizations and independent researchers may not have the necessary resources.

Overfitting to Search Space

NAS can overfit to the search space, meaning the found architecture may perform well on a specific class of problems but not be generalizable.

Stability and Reproducibility

NAS results are sometimes unstable and repeating experiments may lead to different architectures. This makes scientific research challenging.

Limited Interpretability

Architectures found by NAS are often complex and uninterpretable. Understanding why a particular architecture works well is challenging.

Data Dependency

NAS requires a lot of data to find good architectures. In domains where data is limited, NAS may not work well.

The Future of NAS: Trends and Innovations

Efficient and Green NAS

Ongoing research focuses on reducing the computational cost of NAS:
Zero-Cost Proxies: Using training-free metrics to evaluate architectures.
Neural Architecture Transfer: Transferring knowledge from one task to another.
Hardware-Aware NAS: Designing architectures with hardware constraints in mind from the beginning.

NAS for Transformers and Large Models

With the dominance of large language models, NAS is adapting to these architectures:
AutoFormer: NAS for Transformer architectures.
Mixture of Experts Optimization: Optimizing MoE architectures.
Sparse Attention Patterns: Searching for efficient attention patterns.

NAS for Edge and IoT

Designing highly efficient architectures for Edge AI and IoT devices:
MicroNets: Ultra-small networks for microcontrollers.
Quantization-Aware NAS: Designing architectures with quantization in mind.
Multi-Objective NAS: Simultaneously optimizing accuracy, speed, and energy consumption.

Fully Automated NAS

Moving toward fully automated systems that design, train, and deploy architectures without human intervention. This is accelerated with the emergence of AI Agents and Agentic AI.

Combining with New Techniques

NAS + LoRA: Combining NAS with efficient fine-tuning techniques.
NAS + RAG: Designing better architectures for RAG systems.
NAS + Neuromorphic: Designing architectures for neuromorphic computing.

Meta-Learning for NAS

Using meta-learning to learn how to search better. Systems that learn from past experiences and improve the search.

NAS Tools and Frameworks

AutoKeras

One of the friendliest tools to start with NAS. AutoKeras has a simple user interface similar to Keras.

NNI (Neural Network Intelligence)

Microsoft's framework for AutoML with strong NAS support. NNI has great flexibility for implementing custom algorithms.

Auto-PyTorch

A NAS package for PyTorch integrated with the PyTorch ecosystem.

AutoGluon

Amazon's AutoML framework that includes NAS capabilities and is suitable for business users.

TensorFlow and AutoML

TensorFlow has built-in tools for NAS, including TF-NAS.

How to Get Started with NAS: A Practical Guide

For Researchers

  1. Start with Simple Implementations: Implement basic algorithms like Random Search.
  2. Use Frameworks: Use tools like NNI or AutoKeras.
  3. Experiment with Different Search Spaces: Understand the impact of search space design.
  4. Study Found Architectures: Analyze why specific architectures work well.

For Developers

  1. Use AutoML Platforms: Services like Google Cloud AutoML.
  2. Transfer Learning: Use pre-found architectures like EfficientNet.
  3. Start with Small Tasks: Test NAS on smaller datasets.
  4. Monitor Resources: Manage computational cost and time.

For Companies

  1. Need Assessment: Do you really need NAS? Sometimes standard architectures are sufficient.
  2. Start with POC: Small pilot projects before large investments.
  3. Use Cloud Services: Reduce hardware costs using cloud platforms.
  4. Team Training: Invest in training staff for effective NAS use.

Comparing NAS with Other Methods

NAS vs. Manual Design

NAS Advantages:
  • Discovering non-intuitive architectures
  • Reducing development time in the long run
  • Better performance in many cases
  • Scalability for new problems
Manual Design Advantages:
  • Better interpretability
  • More precise control over architecture
  • Lower initial cost
  • Domain-specific knowledge

NAS vs. Transfer Learning

Transfer Learning and NAS complement each other, not compete. You can use an architecture found by NAS through transfer learning.

NAS vs. Hyperparameter Tuning

NAS goes beyond hyperparameter tuning - searching the architecture space is much more complex. But many hyperparameter optimization techniques also apply to NAS.

Success Stories and Case Studies

Google's ImageNet Success

NASNet and EfficientNet architectures set new records on the ImageNet dataset and showed that NAS can compete with the best human designs.

Mobile Optimization

Companies like Google and Apple have used NAS to design optimal models for mobile devices. MobileNetV3 is one of the prominent examples.

Medical Applications

In diagnosing cancer from pathology images, NAS architectures have improved diagnostic accuracy and helped physicians.

Autonomous Systems

In the automotive industry and autonomous vehicles, NAS is used to design efficient architectures for real-time video processing.

Ethical and Social Considerations

Access and Digital Divide

The high computational cost of NAS can deepen the gap between large organizations and independent researchers. This raises concerns about technology democratization.

Environmental Impact

High energy consumption of NAS contributes to the negative impacts of artificial intelligence on the environment. Developing more efficient methods and using renewable energy is essential.

Transparency and Interpretability

Complex architectures found by NAS can exacerbate interpretability problems, which is concerning in sensitive applications like medicine and justice.

Ethics in Artificial Intelligence

Using NAS must be accompanied by ethical principles, including ensuring no bias in found architectures.

Conclusion: NAS and the Future of Model Design

Neural Architecture Search is a major step toward fully automating the machine learning model development process. This technology not only reduces time and cost but also paves the way for discovering innovative architectures that may never be designed by humans.
However, NAS is still in its early stages of development. Challenges such as computational cost, limited interpretability, and unequal access must be solved. The future of NAS will likely include:
  • More Efficient Methods that minimize computational cost
  • Integration with Emerging Technologies like quantum computing and Neuromorphic Computing
  • Friendlier Tools that make NAS accessible to everyone
  • Adaptive Architectures that can adapt themselves to different conditions
NAS is part of a larger trend: moving toward self-improving AI systems that can optimize themselves without human intervention. This may lead to AGI and beyond.
For those working in artificial intelligence, understanding NAS is no longer a choice but a necessity. This technology is changing how we design, develop, and deploy deep learning models and will play an even more important role in the coming years.
The future of neural network design is automated, and NAS is at the center of this transformation. The question is not whether NAS will replace manual design, but how quickly this will happen and how we can best use this power.