91 citations · 644 across the 62 of their papers we have counts for
11 papers · 1 filter
NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants
Zhongzhi Yu, Yonggan Fu, Jiayi Yuan +2
Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it i…
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan +7
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability…
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
Haoran You, Huihong Shi, Yipin Guo +1
Vision Transformers (ViTs) have shown impressive performance and have become a unified backbone for multiple vision tasks. However, both the attention mechanism and multi-layer per…
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Yonggan Fu, Ye Yuan, Souvik Kundu +3
Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and…
Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory Processing
Yang Zhao, Shang Wu, Jingqun Zhang +3
Instant on-device Neural Radiance Fields (NeRFs) are in growing demand for unleashing the promise of immersive AR/VR experiences, but are still limited by their prohibitive trainin…
Hint-Aug: Drawing Hints from Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient Tuning
Zhongzhi Yu, Shang Wu, Yonggan Fu +2
Despite the growing demand for tuning foundation vision transformers (FViTs) on downstream tasks, fully unleashing FViTs' potential under data-limited scenarios (e.g., few-shot tun…
Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction
Sixu Li, Chaojian Li, Wenbo Zhu +6
Neural Radiance Field (NeRF) based 3D reconstruction is highly desirable for immersive Augmented and Virtual Reality (AR/VR) applications, but achieving instant (i.e., < 5 seconds)…
Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design
Yonggan Fu, Zhifan Ye, Jiayi Yuan +4
Novel view synthesis is an essential functionality for enabling immersive experiences in various Augmented- and Virtual-Reality (AR/VR) applications, for which generalizable Neural…
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence
Yonggan Fu, Yuecheng Li, Chenghui Li +4
Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one k…
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning
Yonggan Fu, Ye Yuan, Shang Wu +2
Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks. Ho…
ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement
Chaojian Li, Wenwan Chen, Jiayi Yuan +2
Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement ha…