Fast and resource-efficient Deep Neural Network on FPGA for the Phase-II Level-0 muon barrel trigger of the ATLAS experiment

Open Access

Issue		EPJ Web Conf. Volume 245, 2020 24^th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)


Article Number		01021
Number of page(s)		7
Section		1 - Online and Real-time Computing
DOI		https://doi.org/10.1051/epjconf/202024501021
Published online		16 November 2020

ATLAS Collaboration, The ATLAS Experiment at the CERN Large Hadron Collider, JINST, 3 S08003, (2008). [Google Scholar]
ATLAS Collaboration, Technical Design Report for the Phase-II Upgrade of the ATLAS Muon Spectrometer, Technical Report CERN-LHCC-2017-017, ATLAS-TDR-026, (2017). [Google Scholar]
ATLAS Collaboration, Technical Design Report for the Phase-II Upgrade of the ATLAS TDAQ System, Technical Report CERN-LHCC-2017-020, ATLAS-TDR-029, (2017). [Google Scholar]
M. Courbariaux, Y. Bengio, and J.-P. David, Binaryconnect: Training deep neural networks with binary weights during propagations. In Advances in Neural Information Processing Systems, pages 3123-3131, (2015). [Google Scholar]
F. Li and B. Liu, Ternary Weight Networks, arXiv:1605.04711 [cs.CV], (2016). [Google Scholar]
E. Nurvitadhi et al., Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?, Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 5-14, (2017). [CrossRef] [Google Scholar]
N. Suda, et al., Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks, Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, (2016). [Google Scholar]
T. Boser, P. Calafiura and I. Johnson, Convolutional neural networks for track reconstruction on fpgas, NIPS 2017, (2017). [Google Scholar]
J. Duarte et al., Fast inference of deep neural networks in FPGAs for particle physics, JINST, 13, P07027, (2018). [Google Scholar]
N. Nottbeck, C. Schmitt, V. Büscher, Implementation of high-performance, sub-microsecond deep neural networks on FPGAs for trigger applications, JINST, 14, P09014, (2019). [CrossRef] [Google Scholar]
ATLAS Collaboration, L0 Muon Trigger Public Results, (2019). [Google Scholar]
K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv:1409.1556 [cs.CV], (2014). [Google Scholar]
Y. LeCun, Y., Generalization and network design strategies. Technical Report CRG-TR-89-4, University of Toronto, (1989). [Google Scholar]
Y. Zhou, and R. Chellappa, Computation of optical flow using a neural network. In Neural Networks 1988, IEEE International Conference on, pages 71-78. IEEE, (1988). [Google Scholar]
X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks. In AISTATS’2011, (2011). [Google Scholar]
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. The MIT Press, (2016). [Google Scholar]
S. Ioffe, S. Christian, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs.LG], (2015). [Google Scholar]
D.P. Kingma, and J. Ba, Adam: A method for stochastic optimization. arXiv:1412.6980 [cs.LG] (2014). [Google Scholar]
Xilinx, Vivado Design Suite User Guide - High-Level Synthesis, (2012). [Google Scholar]

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