Resource-Efficient Neural Architect Search Under Constraints
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Solution Overview
Problem
Current neural architecture search methods are computationally expensive and time-consuming, making them less practical for widespread use, especially when resource constraints such as model memory, complexity, and power consumption become important considerations in deploying deep neural networks on various hardware platforms.
Innovation Solution
The Resource-Efficient Neural Architect (RENA) framework uses a policy network with network embedding to adapt existing models progressively, modifying reward functions to tailor target models to hardware constraints, and employs reinforcement learning with multi-objective rewards to efficiently search for high-performance neural network architectures under resource constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural architecture search methods are used to find high-performance neural network architectures, then model performance on benchmarks is improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent pre-computes and stores a library of pre-trained neural network architectures with varying complexities before the actual deployment phase. During deployment, the system selects from this pre-computed library rather than performing exhaustive search, significantly reducing computational cost while maintaining high model performance.
Solution Approach 2:
The patent creates copies of pre-trained neural network architectures from a library of pre-computed models. These copies can be deployed without retraining or exhaustive search, reducing computational resources required while preserving the performance benefits of thorough architecture optimization.
2Reliability
If larger and deeper neural networks are designed to improve performance, then model accuracy increases, but resource constraints such as model memory and power consumption are exceeded
Solution Approach 1:
The patent systematically varies key parameters of neural network architectures (such as number of layers, filter sizes, and network depth) within a pre-computed library to create models with different memory footprints and performance characteristics. This allows selection of optimal parameters that balance accuracy requirements with memory constraints without requiring exhaustive search.
Solution Approach 2:
The patent enables dynamic selection of neural network architectures from the library based on the specific deployment constraints and performance requirements. The system can adaptively choose models with appropriate complexity levels, allowing the same infrastructure to serve multiple resource constraints scenarios.
3Reliability
If manual neural network design is performed, then expertise and experience are leveraged, but the process is laborious and time-consuming
Solution Approach 1:
The patent implements an automated system that generates, trains, and evaluates neural network architectures without requiring manual intervention for each design iteration. The system automatically searches through the architecture space, trains models, and selects optimal configurations, reducing design time while maintaining high quality through systematic exploration of the search space.
Solution Approach 2:
The patent replaces manual architectural design processes with automated computational methods. Instead of relying solely on human expertise to iterate through design space, the system uses automated algorithms to explore architectures, train models, and evaluate performance, significantly reducing the time investment required.
4Reliability
If neural architecture search is performed exhaustively to find optimal architectures, then performance under resource constraints is improved, but search time and computational resources increase
Solution Approach 1:
The patent performs exhaustive or near-exhaustive architecture search and training in advance during a pre-computation phase, storing the results in a library. During actual deployment, the system simply retrieves pre-computed architectures rather than performing time-consuming search and training, achieving optimal performance under constraints without the time penalty of exhaustive search at deployment time.
Data Source
AI summary
Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy but lacked consideration of computational resource use. Presented herein are embodiments of a Resource-Efficient Neural Architect (RENA), an efficient resource-constrained NAS using reinforcement learning with network embedding. RENA embodiments use a policy network to process the network embeddings to generate new configurations. Example demonstrates of RENA embodiments on image recognition and keyword spotting (KWS) problems are also presented herein. RENA embodiments can find novel architectures that achieve high performance even with tight resource constraints. For the CIFAR10 dataset, the tested embodiment achieved 2.95% test error when compute intensity is greater than 100 FLOPs/byte, and 3.87% test error when model size was less than 3M parameters. For the Google Speech Commands Dataset, the tested RENA embodiment achieved the state-of-the-art accuracy without resource constraints, and it outperformed the optimized architectures with tight resource constraints.