Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning

• URL: http://arxiv.org/abs/2509.10526v1
• Date: Thu, 04 Sep 2025 15:05:05 GMT
• Title: Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning
• Authors: Dieter Balemans, Thomas Huybrechts, Jan Steckel, Siegfried Mercelis,
• Abstract summary: This paper presents a novel approach to neural network pruning by integrating a graph-based observation space into an AutoML framework.<n>Our framework transforms the pruning process by introducing a graph representation of the target neural network.<n>For the action space we transition from continuous pruning ratios to fine-grained binary action spaces.
• Score: 0.8890833546984916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel approach to neural network pruning by integrating a graph-based observation space into an AutoML framework to address the limitations of existing methods. Traditional pruning approaches often depend on hand-crafted heuristics and local optimization perspectives, which can lead to suboptimal performance and inefficient pruning strategies. Our framework transforms the pruning process by introducing a graph representation of the target neural network that captures complete topological relationships between layers and channels, replacing the limited layer-wise observation space with a global view of network structure. The core innovations include a Graph Attention Network (GAT) encoder that processes the network's graph representation and generates a rich embedding. Additionally, for the action space we transition from continuous pruning ratios to fine-grained binary action spaces which enables the agent to learn optimal channel importance criteria directly from data, moving away from predefined scoring functions. These contributions are modelled within a Constrained Markov Decision Process (CMDP) framework, allowing the agent to make informed pruning decisions while adhering to resource constraints such as target compression rates. For this, we design a self-competition reward system that encourages the agent to outperform its previous best performance while satisfying the defined constraints. We demonstrate the effectiveness of our approach through extensive experiments on benchmark datasets including CIFAR-10, CIFAR-100, and ImageNet. The experiments show that our method consistently outperforms traditional pruning techniques, showing state-of-the-art results while learning task-specific pruning strategies that identify functionally redundant connections beyond simple weight magnitude considerations.

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