Abstract
Extracting the amplitude and time information from the shaped pulse is an important step in nuclear physics experiments. For this purpose, a neural network can be an alternative in off-line data processing. For processing the data in real time and reducing the off-line data storage required in a trigger event, we designed a customized neural network accelerator on a field programmable gate array platform to implement specific layers in a convolutional neural network. The latter is then used in the front-end electronics of the detector. With fully reconfigurable hardware, a tested neural network structure was used for accurate timing of shaped pulses common in front-end electronics. This design can handle up to four channels of pulse signals at once. The peak performance of each channel is 1.665 Giga operations per second at a working frequency of 25 MHz.
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References
R. Grzywacz, Applications of digital pulse processing in nuclear spectroscopy. Nucl. Instrum. Meth. B 204, 649–659 (2003). https://doi.org/10.1016/S0168-583X(02)02146-8
ALICE Collaboration, Performance of the ALICE experiment at the CERN LHC, Int. J. Mod. Phys. A 29: 1430044 (2014). https://doi.org/10.1142/S0217751X14300440
ATLAS collaboration, Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC, Phys. Lett. B716:1–29 (2012) https://doi.org/10.1016/j.physletb.2012.08.020
N. Petrick, A.O. Hero, N.H. Clinthorne et al., A fast least-squares arrival time estimator for scintillation pulses. IEEE Trans. Nucl. Sci. 41(4), 758–761 (1994). https://doi.org/10.1109/23.322802
G. Ripamonti, A. Geraci, Towards real-time digital pulse processing based on least-mean-squares algorithms. Nucl. Instrum. Meth. A 400(2–3), 447–455 (1997). https://doi.org/10.1016/S0168-9002(97)01012-7
M.A. Nelson, B.D. Rooney, D.R. Dinwiddie et al., Analysis of digital timing methods with BaF2 scintillators. Nucl. Instrum. Meth. A 505(1–2), 324–327 (2003). https://doi.org/10.1016/S0168-9002(03)01078-7
H.Q. Huang, X.F. Yang, W.C. Ding et al., Estimation method for parameters of overlapping nuclear pulse signal. Nucl. Sci. Tech. 28, 12 (2017). https://doi.org/10.1007/s41365-016-0161-z
P.C. Ai, D. Wang, G.M. Huang et al., Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approach. J. Instrum. 14, E03001 (2019). https://doi.org/10.1088/1748-0221/14/03/P03002
B. Denby, Neural networks and cellular automata in experimental high energy physics. Comput. Phys. Commun. 49(3), 429–448 (1998). https://doi.org/10.1016/0010-4655(88)90004-5
J. Griffiths, S. Kleinegesse, D. Saunders, et al. Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks. 2018. arXiv:1611.03180
MicroBooNE collaboration, Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber. arXiv:1808.07269v1
T. Roska, G. Bártfai, P. Szolgay et al., A digital multiprocessor hardware accelerator board for cellular neural networks: CNN-HAC. Int. J. Circuit Theory Appl. 20(5), 589–599 (1992). https://doi.org/10.1002/cta.4490200512
Z. Du, R. Fasthuber, T. Chen, et al. ShiDianNao: shifting vision processing closer to the sensor. ISCA ’15: Proceedings of the 42nd Annual International Symposium on Computer Architecture, vol 43, 3, pp 92–104 (2015). https://doi.org/10.1145/2749469.2750389
J. Qiu, J. Wang, S. Yao, et al. Going deeper with embedded FPGA platform for convolutional neural network. Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, (2016). https://doi.org/10.1145/2847263.2847265
P. Judd, A. Delmas, S. Sharify, et al. Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing. (2017). arXiv:1705.00125
A. Coates, P. Baumstarck, Q. Le, et al. Scalable learning for object detection with GPU hardware. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 10: 4287–4293 (2009). https://doi.org/10.1109/IROS.2009.5354084
A. Boutros, S. Yazdanshenas, V. Betz, You cannot improve what you do not measure: FPGA vs. ASIC efficiency gaps for convolutional neural network inference. ACM Trans. Reconfig. Technol. Syst. 11(3), 1–23 (2018). https://doi.org/10.1145/3242898
H. Torii, The ALICE PHOS calorimeter. J. Phys: Conf. Ser. 160, 012045 (2009). https://doi.org/10.1088/1742-6596/160/1/012045
H. Muller, R. Pimenta, Z. Yin et al., Configurable electronics with low noise and 14-bit dynamic range for photodiode-based photon detectors. Nucl. Instrum. Meth. A 565(2), 768–783 (2006). https://doi.org/10.1016/j.nima.2006.05.246
C. Farabet, C. Poulet, J.Y. Han, et al. CNP: An FPGA-based processor for Convolutional Networks. 2009 International Conference on Field Programmable Logic and Applications. https://doi.org/10.1109/FPL.2009.5272559
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
P. Vincent, H. Larochelle, I. Lajoie et al., Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(12), 3371–3408 (2010)
T.C. Deng, Z.B. Hu, An ultra-low-power processor pipeline-structure. Appl. Electron. Tech. 45(6), 50–53 (2019). (in Chinese)
C. Zhang, P. Li, G.Y. Sun, et al. Optimizing FPGA-based accelerator design for deep convolutional neural networks. ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA2015). 161-170 (2015). https://doi.org/10.1145/2684746.2689060
P. Molchanov, S. Tyree, T. Karras, et al. Pruning convolutional neural networks for resource efficient inference. 2016. arXiv:1611.06440
A. Putnam. Large-scale reconfigurable computing in a microsoft datacenter. 2014 IEEE Hot Chips 26 Symposium (HCS). https://doi.org/10.1109/HOTCHIPS.2014.7478819
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This work was supported by the National Natural Science Foundation of China (Nos. 11875146 and 11505074) and National Key Research and Development Program of China (No. 2016YFE0100900).
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Chen, JL., Ai, PC., Wang, D. et al. FPGA implementation of neural network accelerator for pulse information extraction in high energy physics. NUCL SCI TECH 31, 46 (2020). https://doi.org/10.1007/s41365-020-00756-z
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DOI: https://doi.org/10.1007/s41365-020-00756-z