5 citations · 6 across the 9 of their papers we have counts for
9 papers
Deep learning for state estimation of commercial sodium-ion batteries using partial charging profiles: validation with a multi-temperature ageing dataset
Jiapeng Liu, Lunte Li, Jing Xiang +3
Accurately predicting the state of health for sodium-ion batteries is crucial for managing battery modules, playing a vital role in ensuring operational safety. However, highly acc…
Hyperbolic Binary Neural Network
Jun Chen, Jingyang Xiang, Tianxin Huang +2
Binary Neural Network (BNN) converts full-precision weights and activations into their extreme 1-bit counterparts, making it particularly suitable for deployment on lightweight mob…
DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated LLMs with Refined Rotation
Jingyang Xiang, Sai Qian Zhang
Rotating the activation and weight matrices to reduce the influence of outliers in large language models (LLMs) has recently attracted significant attention, particularly in the co…
QQQ: Quality Quattuor-Bit Quantization for Large Language Models
Ying Zhang, Peng Zhang, Mincong Huang +7
Quantization is a proven effective method for compressing large language models. Although popular techniques like W8A8 and W4A16 effectively maintain model performance, they often…
OvSW: Overcoming Silent Weights for Accurate Binary Neural Networks
Jingyang Xiang, Zuohui Chen, Siqi Li +2
Binary Neural Networks~(BNNs) have been proven to be highly effective for deploying deep neural networks on mobile and embedded platforms. Most existing works focus on minimizing q…
AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction
Siqi Li, Jun Chen, Jingyang Xiang +2
Structured pruning methods are developed to bridge the gap between the massive scale of neural networks and the limited hardware resources. Most current structured pruning methods…
CR-SFP: Learning Consistent Representation for Soft Filter Pruning
Jingyang Xiang, Zhuangzhi Chen, Jianbiao Mei +3
Soft filter pruning~(SFP) has emerged as an effective pruning technique for allowing pruned filters to update and the opportunity for them to regrow to the network. However, this p…
MaxQ: Multi-Axis Query for N:M Sparsity Network
Jingyang Xiang, Siqi Li, Junhao Chen +4
N:M sparsity has received increasing attention due to its remarkable performance and latency trade-off compared with structured and unstructured sparsity. However, existing N:M spa…
SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration
Jingyang Xiang, Siqi Li, Jun Chen +4
The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N cons…