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2692597H@student.gla.ac.uk
Research title: A New Point of View of Channel Pruning for Deep Neural Networks
Exploring optimization techniques for efficient TinyML
Exploring Novel Pruning Techniques for Deep Neural Networks and exploring the TinyML on further DNN architectures.
Hu, W., Henderson, P. and Cano, J. (2025) ICE-Pruning: an Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks. In: International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy, 30 Jun - 05 Jul 2025, (Accepted for Publication)
Hu, W., Henderson, P. and Cano Reyes, J. (2024) DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations. In: NeurIPS 2024 Workshop Machine Learning with new Compute Paradigms, Vancouver, Canada, 14 December 2024,
Haris, J., Saha, R. , Hu, W. and Cano, J. (2024) Designing Efficient LLM Accelerators for Edge Devices. ARC-LG: New Approaches for Addressing the Computing Requirements of LLMs and GNNs, Buenos Aires, Argentina, 30 Jun 2024.
Haris, J., Saha, R. , Hu, W. and Cano, J. (2024) Designing Efficient LLM Accelerators for Edge Devices. ARC-LG: New Approaches for Addressing the Computing Requirements of LLMs and GNNs, Buenos Aires, Argentina, 30 Jun 2024.
Hu, W., Henderson, P. and Cano, J. (2025) ICE-Pruning: an Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks. In: International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy, 30 Jun - 05 Jul 2025, (Accepted for Publication)
Hu, W., Henderson, P. and Cano Reyes, J. (2024) DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations. In: NeurIPS 2024 Workshop Machine Learning with new Compute Paradigms, Vancouver, Canada, 14 December 2024,