One-Shot Neural Network Architecture Search for Multi-Task Hardware

The patent applies parameter changes by systematically varying architecture parameters (number of layers, neurons per layer, activation functions, dropout rates) to find optimal network configurations that achieve the desired generalization power while minimizing space and resource requirements. This is done through automated architecture search methods that explore the parameter space to identify efficient network designs tailored to specific hardware constraints.

The patent implements universality by developing architecture search methods that can optimize neural networks for multiple objectives simultaneously (accuracy, space efficiency, energy consumption, inference time). The system creates multi-objective optimization frameworks that evaluate and balance multiple competing requirements, enabling a single architecture search process to produce networks that are universally applicable across different hardware platforms and performance criteria.

The patent uses parameter changes to optimize energy consumption by adjusting network architecture parameters such as the number of layers, neurons, and activation functions. The architecture search process evaluates energy efficiency metrics and selects configurations that minimize current consumption while maintaining the required evaluation accuracy for embedded systems.

The patent applies partial action by implementing pruning techniques that remove redundant neurons and connections from the neural network. This creates a sparse architecture that uses fewer computational resources and consumes less energy, while retaining the essential functionality needed for accurate evaluation of unseen situations in embedded environments.

The patent implements universality by creating a unified architecture search framework that handles multiple objectives simultaneously without requiring scalarization. The system uses Pareto optimization and multi-objective evolutionary algorithms that can evaluate and balance multiple competing goals (accuracy, space, energy, time) in a single search process, making the optimization process both more efficient and more comprehensive.

The patent introduces an intermediary evaluation mechanism that assesses multiple objectives concurrently during the architecture search process. This intermediary layer ranks and compares different architecture candidates based on multiple criteria simultaneously, enabling the system to navigate the complex multi-objective optimization landscape without reducing it to a single scalar value.

The patent applies parameter changes by optimizing neural network architecture parameters to achieve the desired generalization ability with minimal hardware requirements. The architecture search process identifies efficient network configurations that can be implemented on cost-effective hardware platforms, avoiding the need for expensive high-performance hardware by finding software-level optimizations.

The patent implements this principle by designing neural network architectures that are optimized for efficiency and can be deployed on lower-cost hardware platforms. The architecture search process prioritizes finding solutions that work well on budget-friendly hardware, trading off some hardware capabilities for cost savings while maintaining adequate generalization performance through smart architectural design.