18 citations · 35 across the 8 of their papers we have counts for
8 papers
Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training
Zhenglun Kong, Haoyu Ma, Geng Yuan +12
Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit the…
Pruning Adversarially Robust Neural Networks without Adversarial Examples
Tong Jian, Zifeng Wang, Yanzhi Wang +2
Adversarial pruning compresses models while preserving robustness. Current methods require access to adversarial examples during pruning. This significantly hampers training effici…
DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices
Chao Wu, Cheng Ji, Geng Yuan +5
Data compression has been widely adopted to release mobile devices from intensive write pressure. Delta compression is particularly promising for its high compression efficacy over…
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training
Geng Yuan, Yanyu Li, Sheng Li +5
Recently, sparse training has emerged as a promising paradigm for efficient deep learning on edge devices. The current research mainly devotes efforts to reducing training costs by…
SparCL: Sparse Continual Learning on the Edge
Zifeng Wang, Zheng Zhan, Yifan Gong +7
Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the t…
PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems
Qing Jin, Zhiyu Chen, Jian Ren +3
Processing-in-memory (PIM), an increasingly studied neuromorphic hardware, promises orders of energy and throughput improvements for deep learning inference. Leveraging the massive…