41 citations · 110 across the 26 of their papers we have counts for
27 papers
Normalized Low-Rank Adaptation
Jiale Kang, Ziyin Yue, Zheng Zhan +2
While low-rank adaptation (LoRA) is widely used for parameter-efficient model adaptation, how to regularize its training dynamics for stable and effective optimization remains unde…
LightCache: Memory-Efficient, Training-Free Acceleration for Video Generation
Yang Xiao, Gen Li, Kaiyuan Deng +5
Training-free acceleration has emerged as an advanced research area in video generation based on diffusion models. The redundancy of latents in diffusion model inference provides a…
Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation
Zhenglun Kong, Zheng Zhan, Shiyue Hou +10
Large language models (LLMs) have shown remarkable promise but remain challenging to continually improve through traditional finetuning, particularly when integrating capabilities…
Perturbation-efficient Zeroth-order Optimization for Hardware-friendly On-device Training
Qitao Tan, Sung-En Chang, Rui Xia +10
Zeroth-order (ZO) optimization is an emerging deep neural network (DNN) training paradigm that offers computational simplicity and memory savings. However, this seemingly promising…
Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning
Qitao Tan, Jun Liu, Zheng Zhan +6
Large language models (LLMs) excel across various tasks, but standard first-order (FO) fine-tuning demands considerable memory, significantly limiting real-world deployment. Recent…
Fast and Memory-Efficient Video Diffusion Using Streamlined Inference
Zheng Zhan, Yushu Wu, Yifan Gong +7
The rapid progress in artificial intelligence-generated content (AIGC), especially with diffusion models, has significantly advanced development of high-quality video generation. H…
Search for Efficient Large Language Models
Xuan Shen, Pu Zhao, Yifan Gong +7
Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Numerous efficient techniques, including weight pruning, quantization, and disti…
Rethinking Token Reduction for State Space Models
Zheng Zhan, Yushu Wu, Zhenglun Kong +6
Recent advancements in State Space Models (SSMs) have attracted significant interest, particularly in models optimized for parallel training and handling long-range dependencies. A…
Lotus: learning-based online thermal and latency variation management for two-stage detectors on edge devices
Yifan Gong, Yushu Wu, Zheng Zhan +5
Two-stage object detectors exhibit high accuracy and precise localization, especially for identifying small objects that are favorable for various edge applications. However, the h…
Exploring Token Pruning in Vision State Space Models
Zheng Zhan, Zhenglun Kong, Yifan Gong +8
State Space Models (SSMs) have the advantage of keeping linear computational complexity compared to attention modules in transformers, and have been applied to vision tasks as a ne…
Efficient Training with Denoised Neural Weights
Yifan Gong, Zheng Zhan, Yanyu Li +6
Good weight initialization serves as an effective measure to reduce the training cost of a deep neural network (DNN) model. The choice of how to initialize parameters is challengin…
DiffClass: Diffusion-Based Class Incremental Learning
Zichong Meng, Jie Zhang, Changdi Yang +3
Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden a…
EGAN: Efficient Training of Efficient GANs for Image-to-Image Translation
Yifan Gong, Zheng Zhan, Qing Jin +8
One highly promising direction for enabling flexible real-time on-device image editing is utilizing data distillation by leveraging large-scale text-to-image diffusion models to ge…
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
Zifeng Wang, Zheng Zhan, Yifan Gong +4
Rehearsal-based approaches are a mainstay of continual learning (CL). They mitigate the catastrophic forgetting problem by maintaining a small fixed-size buffer with a subset of da…
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…
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…
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…
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration
Yifan Gong, Geng Yuan, Zheng Zhan +9
Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices. However, prior pru…
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Geng Yuan, Xiaolong Ma, Wei Niu +13
Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search
Zheng Zhan, Yifan Gong, Pu Zhao +9
Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learni…
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
Zhengang Li, Geng Yuan, Wei Niu +13
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Pri…
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization
Wei Niu, Pu Zhao, Zheng Zhan +3
High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. However, the constrained computation and storage re…
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
Tianyun Zhang, Xiaolong Ma, Zheng Zhan +7
To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categ…
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework
Yifan Gong, Zheng Zhan, Zhengang Li +8
Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. However, previous pruning methods ma…