33 citations · 85 across the 34 of their papers we have counts for
5 papers · 1 filter
All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management
Yifan Gong, Zheng Zhan, Pu Zhao +6
During the deployment of deep neural networks (DNNs) on edge devices, many research efforts are devoted to the limited hardware resource. However, little attention is paid to the i…
Advancing Model Pruning via Bi-level Optimization
Yihua Zhang, Yuguang Yao, Parikshit Ram +5
The deployment constraints in practical applications necessitate the pruning of large-scale deep learning models, i.e., promoting their weight sparsity. As illustrated by the Lotte…
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed
Hao Cheng, Pu Zhao, Yize Li +4
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full…
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution
Yushu Wu, Yifan Gong, Pu Zhao +7
Deep learning-based super-resolution (SR) has gained tremendous popularity in recent years because of its high image quality performance and wide application scenarios. However, pr…
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization
Yanyu Li, Pu Zhao, Geng Yuan +3
Neural architecture search (NAS) and network pruning are widely studied efficient AI techniques, but not yet perfect. NAS performs exhaustive candidate architecture search, incurri…