Výsledky vyhľadávania - Computing methodologies Computer graphics Graphics systems AND interfaces Graphics process*

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  1. 1

    RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Rendering Autor Li, Chaojian, Li, Sixu, Zhao, Yang, Zhu, Wenbo, Lin, Yingyan

    ISSN: 1558-2434
    Vydavateľské údaje: ACM 29.10.2022
    “…Neural Radiance Field (NeRF) based rendering has attracted growing attention thanks to its state-of-the-art (SOTA) rendering quality and wide applications in…”
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  2. 2

    Mars: A MapReduce Framework on graphics processors Autor He, Bingsheng, Fang, Wenbin, Luo, Qiong, Govindaraju, Naga K., Wang, Tuyong

    Vydavateľské údaje: ACM 01.10.2008
    “…We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed…”
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  3. 3

    Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory Processing Autor Zhao, Yang Katie, Wu, Shang, Zhang, Jingqun, Li, Sixu, Li, Chaojian, Lin, Yingyan Celine

    Vydavateľské údaje: IEEE 09.07.2023
    “… Our profiling analysis reveals a memory-bound inefficiency in NeRF training. To tackle this inefficiency, near-memory processing (NMP…”
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  4. 4

    Harnessing Conventional Video Processing Insights for Emerging 3D Video Generation Models: A Comprehensive Attention-aware Way Autor Zhao, Tianlang, Liu, Jun, Li, Xingyang, Ding, Li, Li, Jinhao, Li, Shuaiheng, Hu, Jinbo, Dai, Guohao

    Vydavateľské údaje: IEEE 22.06.2025
    “… Inspired by the success of conventional video processing, where video compression exploits similarities among patches, we point out that the attention mechanism can also harness the benefits…”
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  5. 5

    GauRast: Enhancing GPU Triangle Rasterizers to Accelerate 3D Gaussian Splatting Autor Li, Sixu, Keller, Ben, Lin, Yingyan Celine, Khailany, Brucek

    Vydavateľské údaje: IEEE 22.06.2025
    “… This work proposes an acceleration strategy that leverages the similarities between the 3DGS pipeline and the highly optimized conventional graphics pipeline in modern GPUs…”
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  6. 6

    Vulkan-Sim: A GPU Architecture Simulator for Ray Tracing Autor Saed, Mohammadreza, Chou, Yuan Hsi, Liu, Lufei, Nowicki, Tyler, Aamodt, Tor M.

    Vydavateľské údaje: IEEE 01.10.2022
    “… have started to make use of ray tracing APIs to bring more realistic graphics to their players…”
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  7. 7

    Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs Autor Wang, Pengyu, Li, Chao, Wang, Jing, Wang, Taolei, Zhang, Lu, Leng, Jingwen, Chen, Quan, Guo, Minyi

    Vydavateľské údaje: IEEE 01.09.2021
    “…Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt computing-intensive algorithms on large-scale graphs…”
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  8. 8

    Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR Autor Feng Zhou, Duh, Henry Been-Lirn, Billinghurst, Mark

    ISBN: 9781424428403, 1424428408
    Vydavateľské údaje: Washington, DC, USA IEEE Computer Society 15.09.2008
    “…Although Augmented Reality technology was first developed over forty years ago, there has been little survey work giving an overview of recent research in the…”
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    Local-GS: An Order-Independent Gaussian Splatting Training Accelerator Exploiting Splat Locality Autor Sun, Yiyang, Zhi, Qinzhe, Jing, Yiqi, Ye, Le, Huang, Ru, Jia, Tianyu

    Vydavateľské údaje: IEEE 22.06.2025
    “…3D Gaussian Splatting has emerged as the SOTA approach for 3D representation and view synthesis. While Gaussian Splatting has demonstrated impressive…”
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  10. 10

    SynGPU: Synergizing CUDA and Bit-Serial Tensor Cores for Vision Transformer Acceleration on GPU Autor Yao, Yuanzheng, Zhang, Chen, Qi, Chunyu, Chen, Ruiyang, Wang, Jun, Fu, Zhihui, Jing, Naifeng, Liang, Xiaoyao, Song, Zhuoran

    Vydavateľské údaje: IEEE 22.06.2025
    “…Vision Transformers (ViTs) have demonstrated remarkable performance in computer vision tasks by effectively extracting global features…”
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    SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Autor Fan, Zichen, Dai, Steve, Venkatesan, Rangharajan, Sylvester, Dennis, Khailany, Brucek

    Vydavateľské údaje: IEEE 22.06.2025
    “…Diffusion models have gained significant popularity in image generation tasks. However, generating high-quality content remains notably slow because it…”
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    GSAcc: Accelerate 3D Gaussian Splatting via Depth Speculation and Gaussian-centric Rasterization Autor Yang, Mengtian, Wang, Yipeng, Lo, Chieh-Pu, Zhang, Xiuhao, Oruganti, Sirish, Kulkarni, Jaydeep P.

    Vydavateľské údaje: IEEE 22.06.2025
    “…D Gaussian Splatting (3DGS) has emerged as a promising real-time photorealistic radiance field rendering technique. Existing GPU and hardware accelerators face…”
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  13. 13

    Boustrophedonic Frames: Quasi-Optimal L2 Caching for Textures in GPUs Autor Joseph, Diya, Aragon, Juan L., Parcerisa, Joan-Manuel, Gonzalez, Antonio

    Vydavateľské údaje: IEEE 21.10.2023
    “… In this paper, however, we surpass this exploration by fabricating a formal proof for a no-overhead quasi-optimal caching technique for caching textures in graphics workloads…”
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    SFLU: Synchronization-Free Sparse LU Factorization for Fast Circuit Simulation on GPUs Autor Zhao, Jianqi, Wen, Yao, Luo, Yuchen, Jin, Zhou, Liu, Weifeng, Zhou, Zhenya

    Vydavateľské údaje: IEEE 05.12.2021
    “…Sparse LU factorization is one of the key building blocks of sparse direct solvers and often dominates the computing time of circuit simulation programs…”
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    Cambricon-D: Full-Network Differential Acceleration for Diffusion Models Autor Kong, Weihao, Hao, Yifan, Guo, Qi, Zhao, Yongwei, Song, Xinkai, Li, Xiaqing, Zou, Mo, Du, Zidong, Zhang, Rui, Liu, Chang, Wen, Yuanbo, Jin, Pengwei, Hu, Xing, Li, Wei, Xu, Zhiwei, Chen, Tianshi

    Vydavateľské údaje: IEEE 29.06.2024
    “… computational redundancy and substantial hardware expenditures.Performing differential computing on input data seems to be a feasible approach for addressing such computational redundancy and improving hardware efficacy…”
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    MHDiff: Memory- and Hardware-Efficient Diffusion Acceleration via Focal Pixel Aware Quantization Autor Qi, Chunyu, Wang, Xuhang, Chen, Ruiyang, Yao, Yuanzheng, Jing, Naifeng, Zhang, Chen, Wang, Jun, Fu, Zhihui, Liang, Xiaoyao, Song, Zhuoran

    Vydavateľské údaje: IEEE 22.06.2025
    “…Diffusion models have demonstrated superior performance in image generation tasks, thus becoming the mainstream model for generative visual tasks. Diffusion…”
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    StocHD: Stochastic Hyperdimensional System for Efficient and Robust Learning from Raw Data Autor Poduval, Prathyush, Zou, Zhuowen, Najafi, Hassan, Homayoun, Houman, Imani, Mohsen

    Vydavateľské údaje: IEEE 05.12.2021
    “…Hyperdimensional Computing (HDC) is a neurally-inspired computation model working based on the observation that the human brain operates on high-dimensional representations of data, called hypervector…”
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    Leveraging Difference Recurrence Relations for High-Performance GPU Genome Alignment Autor Zeni, Alberto, Onken, Seth, Santambrogio, Marco Domenico, Samadi, Mehrzad

    Vydavateľské údaje: ACM 13.10.2024
    “…Genome pairwise sequence alignment is one of the most computationally intensive workloads in many genomic pipelines, often accounting for over 90% of the…”
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    PARO: Hardware-Software Co-design with Pattern-aware Reorder-based Attention Quantization in Video Generation Models Autor Yang, Xinhao, Zhao, Tianchen, Wang, Hongyi, Ma, Wenheng, Zeng, Shulin, Zhu, Zhenhua, Ning, Xuefei, Yang, Huazhong, Wang, Yu

    Vydavateľské údaje: IEEE 22.06.2025
    “… while ensuring efficient hardware processing. To address these issues, we introduce PARO, a video generation accelerator with patternaware reorder-based attention quantization…”
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    DARIS: An Oversubscribed Spatio-Temporal Scheduler for Real-Time DNN Inference on GPUs Autor Babaei, Amir Fakhim, Chantem, Thidapat

    Vydavateľské údaje: IEEE 22.06.2025
    “… In particular, DARIS improves GPU utilization and uniquely analyzes GPU concurrency by oversubscribing computing resources…”
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