arXiv subjects

Zhenkun Fan

Publications and source records attributed to Zhenkun Fan.

4 recordsLinked to original sources

arXiv2026

Paving the Way for Point Cloud Video Representation Learning Using A PDE Model

Investigating spatial-temporal correlations, specifically how spatial points vary over time, is crucial for understanding point cloud videos. Traditional methods, particularly flow-based techniques, struggle with these correlations due to the unordered spatial arrangement of sequential point cloud data. To address this challenge, we propose a novel approach that regularizes spatial-temporal correlation learning by formulating the problem as a solvable Partial Differential Equation (PDE). While PDEs have long been effective in the physical domain, their application to novel sequential data like point cloud video remains underexplored. Inspired by fluid analysis, we construct a simplified PDE, and the process of solving PDE is guided and refined by a contrastive learning structure between the temporal embeddings and the spatial embeddings. With this extra supervision, our method, named MotionPDE, serves as an effective, plug-and-play enhancement module for existing backbone models, adding minimal computational overhead and parameters. Capitalizing on the contrastive learning process, we delve deeper into the self-supervised capabilities of MotionPDE, yielding promising results that underscore its utility and adaptability in point cloud video data interpretation. The code repo with trained checkpoints will be available at https://github.com/zhh6425/motionpde.git for facilitating future research.

cs.CV↗

arXiv2026

SATA: Sparsity-Aware Scheduling for Selective Token Attention

Transformers have become the foundation of numerous state-of-the-art AI models across diverse domains, thanks to their powerful attention mechanism for modeling long-range dependencies. However, the quadratic scaling complexity of attention poses significant challenges for efficient hardware implementation. While techniques such as quantization and pruning help mitigate this issue, selective token attention offers a promising alternative by narrowing the attention scope to only the most relevant tokens, reducing computation and filtering out noise. In this work, we propose SATA, a locality-centric dynamic scheduling scheme that proactively manages sparsely distributed access patterns from selective Query-Key operations. By reordering operand flow and exploiting data locality, our approach enables early fetch and retirement of intermediate Query/Key vectors, improving system utilization. We implement and evaluate our token management strategy in a control and compute system, using runtime traces from selective-attention-based models. Experimental results show that our method improves system throughput by up to 1.76x and boosts energy efficiency by 2.94x, while incurring minimal scheduling overhead.

cs.AR↗

arXiv2025

Cross-Layer Design of Vector-Symbolic Computing: Bridging Cognition and Brain-Inspired Hardware Acceleration

Vector Symbolic Architectures (VSAs) have been widely deployed in various cognitive applications due to their simple and efficient operations. The widespread adoption of VSAs has, in turn, spurred the development of numerous hardware solutions aimed at optimizing their performance. Despite these advancements, a comprehensive and unified discourse on the convergence of hardware and algorithms in the context of VSAs remains somewhat limited. The paper aims to bridge the gap between theoretical software-level explorations and the development of efficient hardware architectures and emerging technology fabrics for VSAs, providing insights from the co-design aspect for researchers from either side. First, we introduce the principles of vector-symbolic computing, including its core mathematical operations and learning paradigms. Second, we provide an in-depth discussion on hardware technologies for VSAs, analyzing analog, mixed-signal, and digital circuit design styles. We compare hardware implementations of VSAs by carrying out detailed analysis of their performance characteristics and tradeoffs, allowing us to extract design guidelines for the development of arbitrary VSA formulations. Third, we discuss a methodology for cross-layer design of VSAs that identifies synergies across layers and explores key ingredients for hardware/software co-design of VSAs. Finally, as a concrete demonstration of this methodology, we propose the first in-memory computing hierarchical cognition hardware system, showcasing the efficiency, flexibility, and scalability of this co-design approach. The paper concludes with a discussion of open research challenges for future explorations.

cs.AR↗

arXiv2024

On Exploring PDE Modeling for Point Cloud Video Representation Learning

Point cloud video representation learning is challenging due to complex structures and unordered spatial arrangement. Traditional methods struggle with frame-to-frame correlations and point-wise correspondence tracking. Recently, partial differential equations (PDE) have provided a new perspective in uniformly solving spatial-temporal data information within certain constraints. While tracking tangible point correspondence remains challenging, we propose to formalize point cloud video representation learning as a PDE-solving problem. Inspired by fluid analysis, where PDEs are used to solve the deformation of spatial shape over time, we employ PDE to solve the variations of spatial points affected by temporal information. By modeling spatial-temporal correlations, we aim to regularize spatial variations with temporal features, thereby enhancing representation learning in point cloud videos. We introduce Motion PointNet composed of a PointNet-like encoder and a PDE-solving module. Initially, we construct a lightweight yet effective encoder to model an initial state of the spatial variations. Subsequently, we develop our PDE-solving module in a parameterized latent space, tailored to address the spatio-temporal correlations inherent in point cloud video. The process of solving PDE is guided and refined by a contrastive learning structure, which is pivotal in reshaping the feature distribution, thereby optimizing the feature representation within point cloud video data. Remarkably, our Motion PointNet achieves an impressive accuracy of 97.52% on the MSRAction-3D dataset, surpassing the current state-of-the-art in all aspects while consuming minimal resources (only 0.72M parameters and 0.82G FLOPs).

cs.CV↗