What Is NAS (Neural Architecture Search )
Neural Architecture Search (NAS) is an area of artificial intelligence that focuses on automating the design of neural network models. Traditionally, designing these models requires significant expertise and experimentation to determine the best structure and parameters. NAS aims to simplify this process by using algorithms to explore various network architectures systematically and automatically, finding the one that performs best for a specific task, such as image recognition or language processing. This can significantly reduce the time and knowledge required to develop effective AI models, making it more accessible to new users and speeding up AI research and development.
*Source: https://google.research
What is Bittensor?
Bittensor is a decentralized network that enables machine learning models to interact and transact in a blockchain-based ecosystem. In this system, models, referred to as "neurons," communicate with each other to share information and services. The underlying blockchain technology ensures the transactions are secure, traceable, and transparent. Bittensor aims to democratize AI by decentralizing the training and deployment of machine learning models, reducing reliance on centralized data and computation resources.
Why Distributed NAS?
This distributed approach to NAS allows for a more efficient exploration of the architecture space because it leverages the collective computational power of many miners rather than relying on a single resource. By distributing NAS tasks, the Bittensor ecosystem can achieve faster convergence on optimal network architectures, which can be especially beneficial for end-users who require high-performing AI models. This setup not only speeds up the search for efficient neural architectures but also enhances the scalability of AI model development within the decentralized network.
What is benefit of Running NAS on Bittensor
Integrating Neural Architecture Search (NAS) into the Bittensor ecosystem addresses the high costs associated with traditional NAS, which typically requires expensive GPU clusters. By distributing NAS tasks across a decentralized network of compute miners in Bittensor, computational loads are shared, significantly reducing costs and speeding up the search process. This democratizes access to advanced AI technologies, allowing a broader range of developers and researchers to engage in state-of-the-art neural network design at a fraction of the usual cost and time.
Google Cloud Estimate
NASChain Estimate
Image Classification(ImageNet)
$23,000
Object Detection
$36,000
3D Lidar Object Detection
$130,000
$5500
$10000
$35000
What is the outcome of NAS
Pareto front resulting from a genetic algorithm applied to the search for neural network architectures, where each circle represents a unique neural network architecture, termed a genome. These genomes are evaluated based on their memory and computational footprint by miners operating in a decentralized fashion. The Pareto front illustrates the trade-offs between these two critical metrics, showcasing architectures that achieve optimal balances. This visual representation helps in identifying the most efficient architectures from a large set of possibilities, guiding decisions towards those that maximize performance while minimizing resource consumption.
Subset Compute Availability
236
Miners
5
Terabyte GPU VRAM
236
RTX4090 GPUs
24
Validators
2
Days Average Finish Time