arXiv Machine Learning July 14, 2026 By Hao Kong, Di Liu, Xiangzhong Luo, Shuo Huai, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu

Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning

Read the original on arXiv Machine Learning →

arXiv:2607. 11473v1 Announce Type: cross Abstract: In this paper, we propose TECO, a multi-dimensional pruning framework to collaboratively prune the three dimensions (depth, width, and resolution) of convolutional neural networks (CNNs) for better execution efficiency on embedded hardware.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers

arXiv:2606. 03428v1 Announce Type: cross Abstract: The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression.

By Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio, Muhammad Shafique

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One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

DYNA-PRUNER: Input-Adaptive Data-Model Co-Pruning for Efficient and Scalable Spatio-Temporal Media Prediction

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