| hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices F Fahim, B Hawks, C Herwig, J Hirschauer, S Jindariani, N Tran, ... arXiv preprint arXiv:2103.05579, 2021 | 138 | 2021 |
| Applications and techniques for fast machine learning in science AMC Deiana, N Tran, J Agar, M Blott, G Di Guglielmo, J Duarte, P Harris, ... Frontiers in big Data 5, 2022 | 54 | 2022 |
| Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference N Tran, B Hawks, JM Duarte, NJ Fraser, A Pappalardo, Y Umuroglu Frontiers in Artificial Intelligence 4, 94, 2021 | 47 | 2021 |
| GPU-accelerated machine learning inference as a service for computing in neutrino experiments M Wang, T Yang, MA Flechas, P Harris, B Hawks, B Holzman, K Knoepfel, ... Frontiers in big Data 3, 604083, 2021 | 28 | 2021 |
| Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark H Borras, G Di Guglielmo, J Duarte, N Ghielmetti, B Hawks, S Hauck, ... arXiv preprint arXiv:2206.11791, 2022 | 13 | 2022 |
| Qonnx: Representing arbitrary-precision quantized neural networks A Pappalardo, Y Umuroglu, M Blott, J Mitrevski, B Hawks, N Tran, ... arXiv preprint arXiv:2206.07527, 2022 | 11 | 2022 |
| FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning J Duarte, N Tran, B Hawks, C Herwig, J Muhizi, S Prakash, VJ Reddi arXiv preprint arXiv:2207.07958, 2022 | 8 | 2022 |
| hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices. arXiv F Fahim, B Hawks, C Herwig, J Hirschauer, S Jindariani, N Tran, ... arXiv preprint arXiv:2103.05579, 2021 | 5 | 2021 |
| Fkeras: A sensitivity analysis tool for edge neural networks O Weng, A Meza, Q Bock, B Hawks, J Campos, N Tran, JM Duarte, ... Journal on Autonomous Transportation Systems, 2024 | 1 | 2024 |
| hls4ml Demo Lab for DEFCON 30 A Meza, B Hawks Fermi National Accelerator Lab.(FNAL), Batavia, IL (United States), 2022 | 1* | 2022 |
| Real-time machine learning inferencing with edge computing devices from google and intel B Hawks, P Jasal, M Wang, B Nord Fermi National Accelerator Lab.(FNAL), Batavia, IL (United States), 2019 | 1 | 2019 |
| Reliable edge machine learning hardware for scientific applications T Baldi, J Campos, B Hawks, J Ngadiuba, N Tran, D Diaz, J Duarte, ... 2024 IEEE 42nd VLSI Test Symposium (VTS), 1-5, 2024 | | 2024 |
| Applications of Deep Learning to physics workflows M Agarwal, J Alameda, J Audenaert, W Benoit, D Beveridge, ... arXiv preprint arXiv:2306.08106, 2023 | | 2023 |
| submitter: Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark H Borras, R Kastner, T Nguyen, M Blott, N Tran, R Roy, Y Umuroglu, ... | | 2022 |
| Exploring FPGA in-storage computing for Supernova Burst detection in LArTPCs [Poster] J Mitrevski, B Hawks, T Cai, PF Ding, T Junk, K Scholberg, J Shen, N Tran, ... Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States), 2022 | | 2022 |
| Applications and techniques for fast machine learning in science AMC Deiana, N Tran, J Agar, M Blott, G Di Guglielmo, J Duarte, P Harris, ... Frontiers in big Data 5, 2022 | | 2022 |
Javier M. DuarteAssistant Professor, UC San Diego在 ucsd.edu 的电子邮件经过验证
