# Bonsai: A Framework for Convolutional Neural Network Acceleration Using Criterion-Based Pruning

arXiv: https://arxiv.org/abs/2602.17145 · HTML: https://mlsanity.com/paper/2602.17145/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures](https://mlsanity.com/paper/1607.03250/) (2016)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Dynamic Channel Pruning: Feature Boosting and Suppression](https://mlsanity.com/paper/1810.05331/) (2018)
- [Approximated Oracle Filter Pruning for Destructive CNN Width Optimization](https://mlsanity.com/paper/1905.04748/) (2019)
- [LocNet: Improving Localization Accuracy for Object Detection](https://mlsanity.com/paper/1511.07763/) (2015)
- [Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning](https://mlsanity.com/paper/1904.03949/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Multimodal Framework for Aligning Human Linguistic Descriptions with Visual Perceptual Data](https://mlsanity.com/paper/2602.19562/) (2026)
- [LegoLM: Structured Weight Sharing for Large Language Models](https://mlsanity.com/paper/2608.08652/) (2026)
- [Activation-Energy Pruning for Spiking Neural Networks: Unsupervised Personalization via Spike-Count Saliency](https://mlsanity.com/paper/2609.26167/) (2026)

## Abstract (arXiv)

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%.