8 citations · 33 across the 9 of their papers we have counts for
4 papers · 1 filter
Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning
Hao Kong, Di Liu, Xiangzhong Luo +5
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…
FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection
Vikash Sathiamoorthy, Shuo Huai, Hao Kong +7
Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with o…
EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI
Hao Kong, Di Liu, Shuo Huai +5
Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment…
Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardware
Hao Kong, Di Liu, Shuo Huai +5
Scaling down the resolution of input images can greatly reduce the computational overhead of convolutional neural networks (CNNs), which is promising for edge AI. However, as an im…