Výsledky vyhledávání - Computing methodologies Computer graphics Graphics systems and interfaces Graphics processors

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    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
    Vydáno: 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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    Fine-grained DRAM: energy-efficient DRAM for extreme bandwidth systems Autor O'Connor, Mike, Chatterjee, Niladrish, Lee, Donghyuk, Wilson, John, Agrawal, Aditya, Keckler, Stephen W., Dally, William J.

    ISBN: 1450349528, 9781450349529
    ISSN: 2379-3155
    Vydáno: New York, NY, USA ACM 14.10.2017
    “…Future GPUs and other high-performance throughput processors will require multiple TB/s of bandwidth to DRAM…”
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    Mars: A MapReduce Framework on graphics processors Autor He, Bingsheng, Fang, Wenbin, Luo, Qiong, Govindaraju, Naga K., Wang, Tuyong

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

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

    Vydáno: 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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    MCM-GPU: Multi-chip-module GPUs for continued performance scalability Autor Arunkumar, Akhil, Bolotin, Evgeny, Cho, Benjamin, Milic, Ugljesa, Ebrahimi, Eiman, Villa, Oreste, Jaleel, Aamer, Wu, Carole-Jean, Nellans, David

    Vydáno: ACM 01.06.2017
    “…Historically, improvements in GPU-based high performance computing have been tightly coupled to transistor scaling…”
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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

    Vydáno: 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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    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

    Vydáno: 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

    Vydáno: 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.

    Vydáno: 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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    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

    Vydáno: 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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    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

    Vydáno: 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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