26 citations · 46 across the 5 of their papers we have counts for
5 papers
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning
Xiaoyu Gan, Jingbo Jiang, Jingyang Zhu +3
Substantial efforts have been devoted to alleviating the impact of the long-tailed class distribution in federated learning. In this work, we observe an interesting phenomenon that…
Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation
Xizi Chen, Jingyang Zhu, Jingbo Jiang +1
The unstructured sparsity after pruning poses a challenge to the efficient implementation of deep learning models in existing regular architectures like systolic arrays. On the oth…
Fast Convergence Algorithm for Analog Federated Learning
Shuhao Xia, Jingyang Zhu, Yuhan Yang +3
In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end d…
CompRRAE: RRAM-based Convolutional Neural Network Accelerator with Reduced Computations through a Runtime Activation Estimation
Xizi Chen, Jingyang Zhu, Jingbo Jiang +1
Recently Resistive-RAM (RRAM) crossbar has been used in the design of the accelerator of convolutional neural networks (CNNs) to solve the memory wall issue. However, the intensive…
SparseNN: An Energy-Efficient Neural Network Accelerator Exploiting Input and Output Sparsity
Jingyang Zhu, Jingbo Jiang, Xizi Chen +1
Contemporary Deep Neural Network (DNN) contains millions of synaptic connections with tens to hundreds of layers. The large computation and memory requirements pose a challenge to…