Hardware-Aware Neural Network Sparsity for Target Platform Execution

The patent applies preliminary action by incorporating hardware architecture information into the model training process beforehand. The training controller receives hardware configuration data and uses it to guide the reinforcement learning agent in optimizing sparsity ratios during training, rather than attempting to optimize after training is complete. This preliminary integration of hardware awareness enables the model to be pre-optimized for target hardware, resolving the contradiction between training simplicity and execution performance.

The patent employs parameter changes by dynamically adjusting sparsity ratios based on hardware architecture characteristics. The reinforcement learning agent learns to modify the sparsity parameters of different network layers according to the specific hardware platform's features (such as GPU vs. CPU architectures). This parameter optimization during training improves execution performance on target hardware without requiring complete retraining, thus managing the trade-off between training complexity and performance.

The patent applies local quality by implementing different sparsity ratios for different layers of the neural network based on their specific characteristics and the target hardware architecture. Rather than applying uniform sparsity across all layers, the reinforcement learning agent learns to assign appropriate sparsity levels to specific layers (e.g., higher sparsity for convolutional layers, lower for fully connected layers) considering the hardware's computational capabilities. This localized optimization maintains model size reduction while preserving execution efficiency on the target hardware platform.

The patent employs dynamics by making sparsity ratios adaptive rather than static. The reinforcement learning framework enables the system to dynamically determine optimal sparsity ratios during training based on hardware configuration and performance feedback. This dynamic approach allows the model to achieve efficient execution on specific hardware platforms while maintaining reasonable model size, resolving the contradiction between compression and efficiency.

The patent applies feedback by implementing a reinforcement learning framework where the training controller receives feedback about hardware execution performance and uses it to adjust sparsity ratios iteratively. The system monitors the performance impact of different sparsity configurations on the target hardware and feeds this information back to the agent, which then refines its sparsity decisions. This feedback mechanism enables the system to achieve optimal execution performance while managing training time by learning from performance outcomes rather than exhaustive searching.

The patent employs partial action by applying reinforcement learning optimization selectively to critical layers of the neural network rather than uniformly to all layers. The system identifies which layers benefit most from hardware-aware sparsity optimization and focuses computational resources there, while using simpler optimization methods for less critical layers. This partial application of the complex reinforcement learning process reduces overall training time while still achieving significant execution performance improvements on target hardware.