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Dylan S. Rankin
Person information
- affiliation: Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- affiliation: Boston University, Department of Physics, MA, USA
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2020 – today
- 2024
- [j8]Ryan Raikman
, Eric A. Moreno
GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders. Mach. Learn. Sci. Technol. 5(2): 25020 (2024) - [i11]Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip C. Harris:
MACK: Mismodeling Addressed with Contrastive Knowledge. CoRR abs/2410.13947 (2024) - 2023
- [j7]Elham E Khoda
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml. Mach. Learn. Sci. Technol. 4(2): 25004 (2023) - 2022
- [j6]Naif Tarafdar, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Vladimir Loncar, Dylan S. Rankin, Nhan Tran, Zhenbin Wu, Qianfeng Shen, Paul Chow
AIgean: An Open Framework for Deploying Machine Learning on Heterogeneous Clusters. ACM Trans. Reconfigurable Technol. Syst. 15(3): 23:1-23:32 (2022) - [c4]Alec Gunny, Dylan S. Rankin, Philip C. Harris, Erik Katsavounidis, Ethan Marx, Muhammed Saleem
A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics. FlexScience@HPDC 2022: 9-17 - [i10]Philip C. Harris, Erik Katsavounidis, William Patrick McCormack, Dylan S. Rankin
Physics Community Needs, Tools, and Resources for Machine Learning. CoRR abs/2203.16255 (2022) - [i9]Elham E Khoda, Dylan S. Rankin, Rafael Teixeira de Lima, Philip C. Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar, Chaitanya Paikara, Richa Rao, Sioni Summers, Caterina Vernieri, Aaron Wang:
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml. CoRR abs/2207.00559 (2022) - 2021
- [j5]Jennifer Ngadiuba
Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml. Mach. Learn. Sci. Technol. 2(1): 15001 (2021) - [j4]Jeffrey D. Krupa
GPU coprocessors as a service for deep learning inference in high energy physics. Mach. Learn. Sci. Technol. 2(3): 35005 (2021) - [j3]Thea Aarrestad
Fast convolutional neural networks on FPGAs with hls4ml. Mach. Learn. Sci. Technol. 2(4): 45015 (2021) - [i8]Thea Aarrestad
Fast convolutional neural networks on FPGAs with hls4ml. CoRR abs/2101.05108 (2021) - [i7]Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Dylan S. Rankin, Manuel Blanco Valentin, Josiah D. Hester, Yingyi Luo, John Mamish, Seda Ogrenci Memik, Thea Aarrestad
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices. CoRR abs/2103.05579 (2021) - [i6]Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier M. Duarte
Applications and Techniques for Fast Machine Learning in Science. CoRR abs/2110.13041 (2021) - 2020
- [j2]Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. Frontiers Big Data 3: 598927 (2020) - [c3]Naif Tarafdar, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Vladimir Loncar, Dylan S. Rankin
AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters. FCCM 2020: 239 - [c2]Dylan S. Rankin
FPGAs-as-a-Service Toolkit (FaaST). H2RC@SC 2020: 38-47 - [i5]Sioni Summers, Giuseppe Di Guglielmo, Javier M. Duarte
Fast inference of Boosted Decision Trees in FPGAs for particle physics. CoRR abs/2002.02534 (2020) - [i4]Giuseppe Di Guglielmo, Javier M. Duarte
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. CoRR abs/2003.06308 (2020) - [i3]Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. CoRR abs/2008.03601 (2020) - [i2]Dylan Sheldon Rankin, Jeffrey D. Krupa, Philip C. Harris, Maria Acosta Flechas, Burt Holzman, Thomas Klijnsma, Kevin Pedro
FPGAs-as-a-Service Toolkit (FaaST). CoRR abs/2010.08556 (2020) - [i1]Aneesh Heintz, Vesal Razavimaleki, Javier M. Duarte
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs. CoRR abs/2012.01563 (2020)
2010 – 2019
- 2019
- [j1]Javier M. Duarte
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing. Comput. Softw. Big Sci. 3(1) (2019) - [c1]Javier M. Duarte, Song Han, Philip C. Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan S. Rankin, Ryan A. Rivera, Sioni Summers, Nhan Tran, Zhenbin Wu:
Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications. FPGA 2019: 305
Coauthor Index
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