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[2602.17145] Bonsai: A Framework for Convolutional Neural Network Acceleration Using Criterion-Based Pruning
Summary
The paper introduces Bonsai, a framework for accelerating Convolutional Neural Networks (CNNs) through criterion-based pruning, demonstrating significant reductions in model size and computational requirements while maintaining accuracy.
Why It Matters
As AI models grow in complexity, optimizing their performance and efficiency becomes crucial. Bonsai addresses the challenges of pruning by providing a standardized approach, which can enhance model deployment in resource-constrained environments, making it relevant for both researchers and practitioners in AI.
Key Takeaways
- Bonsai framework enables effective criterion-based pruning of CNNs.
- The framework can prune up to 79% of filters while retaining or improving accuracy.
- It reduces computational requirements by up to 68%, enhancing efficiency.
- Introduces a standard language for comparing different pruning criteria.
- Demonstrates varying effects of pruning criteria across different models.
Computer Science > Artificial Intelligence arXiv:2602.17145 (cs) [Submitted on 19 Feb 2026] Title:Bonsai: A Framework for Convolutional Neural Network Acceleration Using Criterion-Based Pruning Authors:Joseph Bingham, Sam Helmich View a PDF of the paper titled Bonsai: A Framework for Convolutional Neural Network Acceleration Using Criterion-Based Pruning, by Joseph Bingham and Sam Helmich View PDF HTML (experimental) Abstract:As the need for more accurate and powerful Convolutional Neural Networks (CNNs) increases, so too does the size, execution time, memory footprint, and power consumption. To overcome this, solutions such as pruning have been proposed with their own metrics and methodologies, or criteria, for how weights should be removed. These solutions do not share a common implementation and are difficult to implement and compare. In this work, we introduce Combine, a criterion- based pruning solution and demonstrate that it is fast and effective framework for iterative pruning, demonstrate that criterion have differing effects on different models, create a standard language for comparing criterion functions, and propose a few novel criterion functions. We show the capacity of these criterion functions and the framework on VGG inspired models, pruning up to 79\% of filters while retaining or improving accuracy, and reducing the computations needed by the network by up to 68\%. Comments: Subjects: Artificial Intelligence (cs.AI) ACM classes: I.2.1 Cite as: arXiv:2602...
