8 papers
HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic execution methods are promis…
Variance-Based Pruning for Accelerating and Compressing Trained Networks
Uranik Berisha, Jens Mehnert, Alexandru Paul Condurache
Increasingly expensive training of ever larger models such as Vision Transfomers motivate reusing the vast library of already trained state-of-the-art networks. However, their late…
PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
Convolutional neural networks (CNNs) are crucial for computer vision tasks on resource-constrained devices. Quantization effectively compresses these models, reducing storage size…
Efficient Data Driven Mixture-of-Expert Extraction from Trained Networks
Uranik Berisha, Jens Mehnert, Alexandru Paul Condurache
Vision Transformers have emerged as the state-of-the-art models in various Computer Vision tasks, but their high computational and resource demands pose significant challenges. Whi…
Data-Free Dynamic Compression of CNNs for Tractable Efficiency
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
To reduce the computational cost of convolutional neural networks (CNNs) on resource-constrained devices, structured pruning approaches have shown promise in lowering floating-poin…
Squeeze-and-Remember Block
Rinor Cakaj, Jens Mehnert, Bin Yang
Convolutional Neural Networks (CNNs) are important for many machine learning tasks. They are built with different types of layers: convolutional layers that detect features, dropou…