Demo Data from EDBT 2026 / reviewers in the wild / expert
Bowei Guo
dblp:392/3421
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Efficient and distributed learning · 67% Generative modeling · 33% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Hardware accelerators and domain-specific architectures · 50% Processor architecture and microarchitecture · 50% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
diffusion model |
0.9 | 1 | 2025 | MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics · ICCV 2025 |
Machine learning › Efficient and distributed learning
model compression |
0.9 | 1 | 2025 | MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics · ICCV 2025 |
Machine learning › Efficient and distributed learning › model compression › pruning
structured pruning |
0.9 | 1 | 2025 | MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics · ICCV 2025 |
Processor architecture and microarchitecture
multi-chip architecture |
0.8 | 1 | 2024 | Fusion-3D: Integrated Acceleration for Instant 3D Reconstruction and Real-Time Rendering · MICRO 2024 |
Hardware accelerators and domain-specific architectures › neural rendering accelerator
neural radiance field accelerator |
0.8 | 1 | 2024 | Fusion-3D: Integrated Acceleration for Instant 3D Reconstruction and Real-Time Rendering · MICRO 2024 |
Methods — techniques the papers use, named apart from their topics
structural pruning · 0.9pretraining dynamics analysis · 0.9mixed-precision arithmetic · 0.8hierarchical tiling · 0.8hardware-aware sampling · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics
Bowei Guo, Shengkun Tang, Cong Zeng |
ICCV | 1 |
| 2024 | Fusion-3D: Integrated Acceleration for Instant 3D Reconstruction and Real-Time RenderingabstractRecent breakthroughs in Neural Radiance Field (NeRF) based 3D reconstruction and rendering have spurred the possibility of immersive experiences in augmented and virtual reality (AR/VR). However, current NeRF acceleration techniques are still inadequate for real-world AR/VR applications due to: 1) the lack of end-to-end pipeline acceleration support, which causes impractical off-chip bandwidth demands for edge devices, and 2) limited scalability in handling large-scale scenes. To tackle these limitations, we have developed an end-to-end, scalable 3D acceleration framework called Fusion-3D, capable of instant scene reconstruction and real-time rendering. Fusion-3D achieves these goals through two key innovations: 1) an optimized end-to-end processor for all three stages of the NeRF pipeline, featuring dynamic scheduling and hardware-aware sampling in the first stage, and a shared, reconfigurable pipeline with mixed-precision arithmetic in the second and third stages; 2) a multi-chip architecture for handling large-scale scenes, integrating a three-level hierarchical tiling scheme that minimizes inter-chip communication and balances workloads across chips. Extensive experiments validate the effectiveness of Fusion-3D in facilitating real-time, energy-efficient 3D reconstruction and rendering. Specifically, we tape out a prototype chip in 28nm CMOS to evaluate the effectiveness of the proposed end-to-end processor. Extensive simulation based on the on-silicon measurements demonstrates a$\mathbf{2.5}\times$and$\mathbf{6}\times$throughput improvement in training and inference, respectively, compared to state-of-the-art accelerators. Furthermore, to assess the multi-chip architecture, we integrate four chips into a single PCB as a prototype. Further simulation results show that the multi-chip system achieves a$\mathbf{7.3}\times$and$\mathbf{6.5}\times$throughput improvement in training and inference, respectively, over the Nvidia 2080Ti GPU. To the best of our knowledge, Fusion-3D is the first to achieve both instant (≤ 2 seconds) 3D reconstruction and real-time (≥ 30 FPS) rendering, while only requiring the bandwidth of the most commonly used USB port (0.625 GB/s, 5 Gbps) in edge devices for off-chip communication. Sixu Li, Yang Zhao 0013, Chaojian Li, Bowei Guo, Jingqun Zhang, Zhifan Ye, Cheng Wan 0005, Yingyan (Celine) Lin |
MICRO | 4 |