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Copy file name to clipboardExpand all lines: projects.html
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@@ -48,24 +48,37 @@ <h2>Projects</h2>
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<h3> Papers </h3>
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<pstyle="line-height:1.3">
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<ul>
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<li> GPU-accelerated machine learning inference as a service for computing in neutrino experiments, <ahref="https://arxiv.org/abs/2009.04509">arXiv:2009.04509</a> [physics.comp-ph]. </li>
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<li> Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics, <ahref="https://arxiv.org/abs/2008.03601">arXiv:2008.03601</a> [physics.comp-ph]. </li>
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<li> GPU coprocessors as a service for deep learning inference in high energy physics, <ahref="https://arxiv.org/abs/2007.10359">arXiv:2007.10539</a> [physics.comp-ph]. </li>
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<li> Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml, <ahref="https://arxiv.org/abs/2006.10159">arXiv:2006.10159</a> [physics.ins-det]. </li>
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<li> Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml, <ahref="https://doi.org/10.1088/2632-2153/aba042">MLST (2020)</a>. </li>
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<li> Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster, <ahref="https://arxiv.org/abs/2011.07371">arXiv:2011.07371</a>. </li>
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<li> A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC, <ahref="https://10.1109/TNS.2021.3087100">IEEE Trans. Nucl. Sci. 68, 2179 (2021)</a>. </li>
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<li> Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider, <ahref="https://arxiv.org/abs/2108.03986">arXiv:2108.03986</a>. </li>
<li> hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices, <ahref="https://arxiv.org/abs/2103.05579">arXiv:2103.05579</a>. </li>
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<li> GPU-accelerated machine learning inference as a service for computing in neutrino experiments, <ahref="https://doi.org/10.1038/s42256-021-00356-5">Nat. Mach. Intell. 3, 675 (2021)</a>. </li>
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<li> Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics, <ahref="https://arxiv.org/abs/2008.03601">arXiv:2008.03601</a>. </li>
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<li> GPU coprocessors as a service for deep learning inference in high energy physics, <ahref="https://arxiv.org/abs/2007.10359">arXiv:2007.10539</a>. </li>
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<li> Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors, <ahref="https://arxiv.org/abs/2006.10159">arXiv:2006.10159</a>. </li>
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<li> Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml, <ahref="https://doi.org/10.1088/2632-2153/aba042">Mach. Learn.: Sci. Technol. 2, 015001 (2021)</a>. </li>
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<li> Fast inference of Boosted Decision Trees in FPGAs for particle physics, <ahref="https://doi.org/10.1088/1748-0221/15/05/p05026">JINST 15, P05026 (2020)</a>. </li>
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<li> ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning, <ahref="https://sld.cs.columbia.edu/pubs/giri_date20.pdf"> DATE Conference 2020 </a>.
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<li> ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning, <ahref="https://sld.cs.columbia.edu/pubs/giri_date20.pdf"> DATE Conference 2020 </a>.</li>
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<li> Accelerated Machine Learning as a Service for Particle Physics Computing, <ahref="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_64.pdf">NeurIPS ML4PS Workshop 2019</a>. </li>
<li> Fast inference of deep neural networks in FPGAs for particle physics, <ahref="https://doi.org/10.1088/1748-0221/13/07/P07027">JINST 13, P07027 (2018)</a>. </li>
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<li> FPGA-accelerated machine learning inference as a service for particle physics computing, <ahref="https://doi.org/10.1007/s41781-019-0027-2">Comput. Softw. Big Sci. 3, 13 (2019)</a></li>
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<li> FPGA-accelerated machine learning inference as a service for particle physics computing, <ahref="https://doi.org/10.1007/s41781-019-0027-2">Comput. Softw. Big Sci. 3, 13 (2019)</a>.</li>
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</ul>
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</p>
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<h3> Talks/Videos </h3>
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<pstyle="line-height:1.3">
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List of various presentations from the community<br>
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<ul>
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<li> P. Harris, ML Acceleration with Heterogeneous Computing for Big Data Physics Experiments, Heterogeneous High Performance Computing Workshop at Supercomputing 2019, <ahref="https://h2rc.cse.sc.edu/slides/invited1_Harris.pdf">slides</a></li>
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<li> C. Herwig, An ML Control System for the Fermilab Booster, BIDS Machine Learning and Science Forum, April 2021, <ahref="https://bids.berkeley.edu/events/machine-learning-and-science-forum-2021-0405">abstract</a></li>
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<li> P. Harris, Quick and Quirk with Quarks, IAIFI Colloquium Online, March 2021, <ahref="https://www.youtube.com/watch?v=9_vNh09qqmw">video</a></li>
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<li> P. Harris, Scientific Applications of FPGAs at the LHC, ISFPGA 2021 (keynote), <ahref="https://dl.acm.org/doi/10.1145/3431920.3437119">abstract</a></li>
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<li> J. Duarte, AI at the Edge of Particle Physics, <ahref="https://argonne.zoomgov.com/rec/play/-Zjdo2cH5hq0gPOaG36RLVU_mTGUDjP_w3AuI0pajsw6WTpSiKhX0qLxGLW3gP4xNVkhnIBpv8ebNdw1.oOyAyU_bdLDggX_Q?continueMode=true">video</a></li>
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<li> J. Duarte, hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices, tinyML Research Symposium 2021, <ahref="https://www.youtube.com/watch?v=9p7pRqise8I">video</a></li>
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<li> D. Rankin, FPGAs-as-a-Service Toolkit (FaaST), Heterogeneous High-Performance Reconfigurable Computing Workshop at Supercomputing 2020, <ahref="https://h2rc.cse.sc.edu/2020/slides/08_Rankin.pdf">slides</a></li>
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<li> P. Harris, ML Acceleration with Heterogeneous Computing for Big Data Physics Experiments, Heterogeneous High-Performance Reconfigurable Computing Workshop at Supercomputing 2019, <ahref="https://h2rc.cse.sc.edu/2019/slides/invited1_Harris.pdf">slides</a></li>
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<li> K. Pedro, FPGA-accelerated machine learning inference as a service for particle physics computing, CHEP 2019, <ahref="https://indico.cern.ch/event/773049/contributions/3474731/">slides</a></li>
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<li> J. Duarte, Machine Learning on FPGAs for low latency and high throughput inference, eScience 2019, <ahref="https://escience2019.sched.com/event/Uuiy/machine-learning-on-fpgas-for-low-latency-and-high-throughput-inference?iframe=yes&w=100%&sidebar=yes&bg=no#">slides</a></li>
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<li> M. Liu, FPGA-accelerated machine learning inference as a solution for particle physics computing challenges, PASC 2019, <ahref="https://pasc19.pasc-conference.org/program/schedule/presentation/?id=msa134&sess=sess164">slides</a></li>
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