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Bowei Guo

dblp:392/3421 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
diffusion model
0.912025
MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics · ICCV 2025
Machine learning › Efficient and distributed learning
model compression
0.912025
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
0.912025
MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics · ICCV 2025
Processor architecture and microarchitecture
multi-chip architecture
0.812024
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
0.812024
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
YearPublicationVenuePosition
2025 MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics
Bowei Guo, Shengkun Tang, Cong Zeng
ICCV1
2024 Fusion-3D: Integrated Acceleration for Instant 3D Reconstruction and Real-Time Rendering
abstract
Recent 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
MICRO4