Collaborative Neural Architecture Search Using Variational Autoencoders
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Solution Overview
Problem
Existing neural architecture search techniques are time-consuming and computationally intensive, requiring significant resources and central coordination for evaluating different neural architectures, and lack efficient methods for partitioning the search space and determining optimal architectures.
Innovation Solution
A collaborative neural architecture search method using variational autoencoders to embed neural architectures in a vector space, allowing for distributed training and validation across multiple nodes, with data sharing and gradient-based optimization to improve performance, and leveraging a distributed ledger for minimal communication and probabilistic verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural architecture search evaluates different architectures sequentially, then evaluation accuracy is improved, but search time and computational resources increase significantly
Solution Approach 1:
The patent divides the search space into multiple partitions and assigns different partitions to different worker nodes for parallel evaluation. The master node coordinates the search process by managing worker nodes and aggregating results, enabling simultaneous evaluation of multiple architectures without compromising accuracy
Solution Approach 2:
The patent creates multiple copies of the evaluation pipeline across distributed worker nodes. Each worker node independently evaluates architectures in its assigned partition using the same evaluation criteria, allowing parallel processing while maintaining consistent evaluation standards
2Measurement precision
If more computational resources are allocated to evaluate architectures thoroughly, then performance prediction accuracy is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system segments the computational workload by dividing the search space into partitions and assigning them to different worker nodes. This distributes the computational burden while maintaining thorough evaluation through gradient-based optimization at each node
Solution Approach 2:
The master node acts as an intermediary that coordinates between the search space definition and worker nodes. It manages the distribution of evaluation tasks and aggregation of results, simplifying the overall system architecture while enabling complex distributed computations
3Productivity
If distributed nodes evaluate architectures independently without coordination, then processing speed is improved, but data consistency and search efficiency deteriorate
Solution Approach 1:
Worker nodes continuously report evaluation results and gradient information back to the master node, which coordinates updates to the embedding positions. This feedback mechanism ensures that distributed evaluations remain consistent with the overall search objective while maintaining high processing speed
Solution Approach 2:
The system maintains continuous coordination between the master node and worker nodes through iterative gradient updates and embedding position adjustments. This ensures that the distributed evaluation process remains aligned with the search objective throughout the optimization process
Data Source
AI summary
This application embodiment relates to the field of neural architecture search technology, particularly to an apparatus and method for collaborative neural network search. The apparatus includes a first node for providing a preset search space and dataset, a second node for generating a variational autoencoder and partitioning the search space; at least two cooperating third nodes, the third node is used to determine the performance of multiple candidate neural architectures, train a performance predictor, and update the embedding position based on the gradient direction provided by the performance trainer, wherein data information is shared between different third nodes. By using the variational autoencoder, the third node can hypothesize that similar unit structures/neural architectures have similar performance on the same dataset when conducting the search, collaboratively searching for the best neural architecture (by broadcasting the evaluation results of its neural architecture) and achieving effective evaluation of different neural architectures.