7 citations · 24 across the 8 of their papers we have counts for
8 papers
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
Yonggan Fu, Yongan Zhang, Zhongzhi Yu +5
The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing…
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…
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization
Jose A. Carrillo, Nicolas Garcia Trillos, Sixu Li +1
Federated learning is an important framework in modern machine learning that seeks to integrate the training of learning models from multiple users, each user having their own loca…
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…
RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Rendering
Chaojian Li, Sixu Li, Yang Zhao +2
Neural Radiance Field (NeRF) based rendering has attracted growing attention thanks to its state-of-the-art (SOTA) rendering quality and wide applications in Augmented and Virtual…
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks
Aditya Kumar Akash, Sixu Li, Nicolás García Trillos
Based on the concepts of Wasserstein barycenter (WB) and Gromov-Wasserstein barycenter (GWB), we propose a unified mathematical framework for neural network (NN) model fusion and u…
Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle
Bin Xu, Junzhe Shi, Sixu Li +2
Propulsion system electrification revolution has been undergoing in the automotive industry. The electrified propulsion system improves energy efficiency and reduces the dependence…