Point-based networks achieve high accuracy by preserving the intrinsic spatial structure of point clouds. The spatial information is effectively extracted by set abstraction, a critical module for feature learning in point-based networks. However, set abstraction introduces a computational bottleneck, and naive parallelization often degrades sampling quality, leading to accuracy loss. To address these challenges, we propose Parallel-SA, a framework that accelerates point-based networks by transforming set abstraction from sequential to parallel processing without sacrificing accuracy. Parallel-SA leverages a multi-scale sampling distribution approximation to preserve sampling quality under parallel execution. In addition, it employs distribution-aware balanced partitioning and adaptive load-balancing refinement to further improve efficiency. Experiments show that Parallel-SA achieves an average 2.38× speedup in set abstraction with minimal accuracy degradation.

As a fundamental perception task, 3D point cloud detection has become essential for applications in autonomous driving and robotics. However, point cloud detection faces significant challenges of high computational cost due to complex point processing operations. To address this issue, we propose DAWN, an acceleration framework for point cloud object detection that identifies partial similarities between adjacent frames and reduces computational cost by filtering redundant points. DAWN uses object-aware partitioning that defines boundaries based on previous detection results for localized similarity analysis. Additionally, it applies axis-sorted point selection to refine partitioning for point clouds with non-uniform distribution. An efficient 3D similarity algorithm then filters redundant points to reduce computational load. DAWN enables flexible latencyaccuracy trade-offs by tuning point filtering ratios. Experimental results show that DAWN achieves a $1.59 \times$ average speedup and up to $1.70 \times$ on state-of-the-art detection networks by filtering more than $50 \%$ of points on average, with negligible impact on accuracy.

Against the backdrop of accelerating globalization and increasingly fierce corporate competition, financial management, as the core of corporate governance, is facing growing complexity. Traditional approaches fall short in handling massive financial data, volatile markets, and elevated decision demands, while AI and big data technologies offer robust support for intelligent decision-making. This paper presents a hybrid framework integrating AIGC with financial expertise, leveraging NLP and knowledge base retrieval to enable automated financial knowledge analysis. Experimental results validate its high accuracy in knowledge matching, addressing traditional limitations and advancing the intelligent transformation of financial management.

Log-Structured-Merge-tree (LSM-tree) has been extensively adopted because of its exceptional write efficiency and high space utilization. Compaction is invoked periodically in LSM-tree based key-value(LSM KV) systems to maintain good system performance. As the size of LSM-KV grows, large-scale compaction is now frequently seen. Compaction throughput significantly degrades with larger inputs, leading to frequent write stalls and decrement in overall write throughput. This paper proposes STEM, a stream-based compaction framework with FPGA to address this issue. A clean-cut algorithm is introduced to enable streaming-based compaction for large-scale data. With a multi-unit pipeline and dynamic pipeline schedule, STEM can handle large-scale compaction tasks efficiently. Based on the experiment result, the compaction throughput of STEM can achieve$27\times$on average and up to$35\times$improvement compared with the current RocksDB compaction,$2.09\times$to$2.27\times$improvement compared with the state-of-the-art FPGA accelerator.

In recent years, the demand for data storage space has increased dramatically due to the exponential growth of data volume. Data compression is of great significance since it saves data storage space and reduces data transfer demand. Compression algorithms based on statistical models have a much higher compression ratio than dictionary-based methods, but the high computational time cost of statistical modeling limits their wider application. In this paper, we introduce pLPAQ, an FPGA-based design of a powerful compression algorithm LPAQ based on statistical models. A novel hardware accelerator is proposed to speed up LPAQ by fully utilizing the parallelism of FPGA. Experimental results show that the proposed design can achieve a throughput of 12 MB/s on Xilinx Virtex Plus UltraScale XCVU9P card, 25x faster than executing on AMD Ryzen R7 4800U at 2.8 GHz and 80x faster compared with the naive FPGA implementation on average.

WMN (Wireless Mesh Network) is a useful wireless multi-hop network with tremendous research value. A good routing mechanism for WMN can use the whole bandwidth of the network and can assure the quality of service of traffic. Through simulations we verify that the routing metric ETX (Expected Transmission Count) cannot assure good quality of wireless links. To improve the routing performance, a QoS-assured opportunistic routing mechanism is proposed in this paper. This mechanism can choose the highest throughput links to improve the performance of routing over WMN and then reduce the energy consumption of mesh routers. The analyses show that the opportunistic routing mechanism is better than the mechanism with the routing metric of ETX.