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

    ISSN: 1558-2434
    Published: 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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    Conference Proceeding
  10. 10

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

    ISBN: 1450349528, 9781450349529
    ISSN: 2379-3155
    Published: 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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    Conference Proceeding
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    Mars: A MapReduce Framework on graphics processors by He, Bingsheng, Fang, Wenbin, Luo, Qiong, Govindaraju, Naga K., Wang, Tuyong

    Published: 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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    Conference Proceeding
  12. 12

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

    Published: 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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  13. 13

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

    Published: 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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  14. 14

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

    Published: 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 by Sun, Yiyang, Zhi, Qinzhe, Jing, Yiqi, Ye, Le, Huang, Ru, Jia, Tianyu

    Published: 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 by Yao, Yuanzheng, Zhang, Chen, Qi, Chunyu, Chen, Ruiyang, Wang, Jun, Fu, Zhihui, Jing, Naifeng, Liang, Xiaoyao, Song, Zhuoran

    Published: IEEE 22.06.2025
    “…Vision Transformers (ViTs) have demonstrated remarkable performance in computer vision tasks by effectively extracting global features…”
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    Conference Proceeding
  17. 17

    SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity by Fan, Zichen, Dai, Steve, Venkatesan, Rangharajan, Sylvester, Dennis, Khailany, Brucek

    Published: 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 by Yang, Mengtian, Wang, Yipeng, Lo, Chieh-Pu, Zhang, Xiuhao, Oruganti, Sirish, Kulkarni, Jaydeep P.

    Published: 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 by Wang, Pengyu, Li, Chao, Wang, Jing, Wang, Taolei, Zhang, Lu, Leng, Jingwen, Chen, Quan, Guo, Minyi

    Published: 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 by 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

    Published: 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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    Conference Proceeding