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ORCIDJovan Mitrevski
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2020 – today
- 2024
[j1]Javier Campos, Jovan Mitrevski, Nhan Tran, Zhen Dong, Amir Gholaminejad, Michael W. Mahoney, Javier M. Duarte:
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs. ACM Trans. Reconfigurable Technol. Syst. 17(3): 36:1-36:22 (2024)- [c1]Rui Shi, Seda Ogrenci, J. M. Arnold, J. R. Berlioz, Pierrick Hanlet, Kyle J. Hazelwood, M. A. Ibrahim, Han Liu, V. P. Nagaslaev, Aakaash Narayanan, D. J. Nicklaus, Jovan Mitrevski, Gauri Pradhan, A. L. Saewert, B. A. Schupbach, Kiyomi Seiya, Mattson Thieme, R. M. Thurman-Keup, N. V. Tran:
ML-Based Real-Time Control at the Edge: An Approach Using hls4ml. IPDPS (Workshops) 2024: 191 - 2023
- [i5]Javier Campos, Zhen Dong, Javier M. Duarte, Amir Gholami, Michael W. Mahoney, Jovan Mitrevski, Nhan Tran:
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs. CoRR abs/2304.06745 (2023) - [i4]Rui Shi, Seda Ogrenci, J. M. Arnold, J. R. Berlioz, Pierrick Hanlet, Kyle J. Hazelwood, M. A. Ibrahim, Han Liu, V. P. Nagaslaev, Aakaash Narayanan, D. J. Nicklaus, Jovan Mitrevski, Gauri Pradhan, A. L. Saewert, B. A. Schupbach, Kiyomi Seiya, Mattson Thieme, R. M. Thurman-Keup, N. V. Tran:
ML-based Real-Time Control at the Edge: An Approach Using hls4ml. CoRR abs/2311.05716 (2023) - [i3]Chenwei Xu, Jerry Yao-Chieh Hu, Aakaash Narayanan, Mattson Thieme, Vladimir Nagaslaev, Mark Austin, Jeremy Arnold, Jose Berlioz, Pierrick Hanlet, Aisha Ibrahim, Dennis Nicklaus, Jovan Mitrevski, Jason Michael St. John, Gauri Pradhan, Andrea Saewert, Kiyomi Seiya, Brian Schupbach, Randy Thurman-Keup, Nhan Tran, Rui Shi, Seda Ogrenci, Alexis Maya-Isabelle Shuping, Kyle J. Hazelwood, Han Liu:
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e. CoRR abs/2312.17372 (2023) - 2022
- [i2]Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott, Jovan Mitrevski, Benjamin Hawks
, Nhan Tran, Vladimir Loncar, Sioni Summers, Hendrik Borras, Jules Muhizi, Matthew Trahms, Shih-Chieh Hsu, Scott Hauck, Javier M. Duarte:
QONNX: Representing Arbitrary-Precision Quantized Neural Networks. CoRR abs/2206.07527 (2022) - 2021
- [i1]Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey D. Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric A. Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan S. Rankin, Simon Rothman, Ashish Sharma, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng:
Applications and Techniques for Fast Machine Learning in Science. CoRR abs/2110.13041 (2021)
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