Fetching the paper…
Reading the bibliography…
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC).
Graph processing on FPGAs: Taxonomy, survey, challenges
Besta, M., Stanojevic, D., De Fine Licht, J., Ben-Nun, T., and Hoefler, T. (2019) · 1903
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E. (2019) · 1903
Earlier work this paper cites.
A survey on graph processing accelerators: Challenges and opportunities
Gui, C.-Y., Zheng, L., He, B., Liu, C., Chen, X.-Y., Liao, X.-F., et al. (2019) · 1914
Earlier work this paper cites.
Design of ion-implanted MOSFET’s with very small physical dimensions
Dennard, R. H., Gaensslen, F. H., Yu, H., Rideout, V. L., Bassous, E., and LeBlanc, A. R. (1974) · 1974
Earlier work this paper cites.
Application of Kalman filtering to track and vertex fitting
R. Frühwirth (1987) · 1987
Earlier work this paper cites.
Progressive track recognition with a Kalman-like fitting procedure
Billoir, P. (1989) · 1989
Earlier work this paper cites.
Simultaneous pattern recognition and track fitting by the Kalman filtering method
Billoir, P. and Qian, S. (1990) · 1990
Earlier work this paper cites.
A concurrent track evolution algorithm for pattern recognition in the hera-b main tracking system
Mankel, R. (1997) · 1997
Earlier work this paper cites.
Graph neural networks for particle reconstruction in high energy physics detectors
Ju, X. et al. (2019) · 2003
Earlier work this paper cites.
GRIP: A graph neural network accelerator architecture
Kiningham, K., Re, C., and Levis, P. (2020) · 2007
Earlier work this paper cites.
Rectified linear units improve restricted Boltzmann machines
Nair, V. and Hinton, G. E. (2010) · 2010
Earlier work this paper cites.
Track and vertex reconstruction: From classical to adaptive methods
Strandlie, A. and Frühwirth, R. (2010) · 2010
Earlier work this paper cites.
Dark silicon and the end of multicore scaling
Esmaeilzadeh, H., Blem, E., St. Amant, R., Sankaralingam, K., and Burger, D. (2011) · 2011
Earlier work this paper cites.
Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y. (2011) · 2011
Earlier work this paper cites.
Accelerated charged particle tracking with graph neural networks on FPGAs
Heintz, A., Razavimaleki, V., Duarte, J., DeZoort, G., Ojalvo, I., Thais, S., et al. (2020) · 2012
Earlier work this paper cites.
Description and performance of track and primary-vertex reconstruction with the CMS tracker
CMS Collaboration (2014) · 2014
Earlier work this paper cites.
GraphGen: An FPGA framework for vertex-centric graph computation
Nurvitadhi, E., Weisz, G., Wang, Y., Hurkat, S., Nguyen, M., Hoe, J. C., et al. (2014) · 2014
Earlier work this paper cites.
The CMS High Level Trigger
Trocino, D. (2014) · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Battaglia, P. W., Pascanu, R., Lai, M., Rezende, D. J., and Kavukcuoglu, K. (2016) · 2016
Earlier work this paper cites.
Energy efficient architecture for graph analytics accelerators
Ozdal, M. M., Yesil, S., Kim, T., Ayupov, A., Greth, J., Burns, S., et al. (2016) · 2016
Cited alongside, same era.
Technical Design Report for the Phase-II Upgrade of the ATLAS TDAQ System
ATLAS Collaboration (2017b) · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Battaglia, P. W. et al. (2018) · 2018
Cited alongside, same era.
Fast inference of deep neural networks in FPGAs for particle physics
Duarte, J., Han, S., Harris, P., Jindariani, S., Kreinar, E., Kreis, B., et al. (2018) · 2018
Cited alongside, same era.
Novel deep learning methods for track reconstruction
Farrell, S., Calafiura, P., Mudigonda, M., Prabhat, Anderson, D., Vlimant, J.-R., et al. (2018) · 2018
Cited alongside, same era.
ParticleNet: Jet Tagging via Particle Clouds
Qu, H. and Gouskos, L. (2020) · 2020
Later among the works it cites.
Graph neural networks in particle physics
Shlomi, J., Battaglia, P., and Vlimant, J.-R. (2020) · 2020
Later among the works it cites.
Fast inference of boosted decision trees in FPGAs for particle physics
Summers, S., Di Guglielmo, G., Duarte, J., Harris, P., Hoang, D., Jindariani, S., et al. (2020) · 2020
Later among the works it cites.
HyGCN: A GCN accelerator with hybrid architecture
Yan, M., Deng, L., Hu, X., Liang, L., Feng, Y., Ye, X., et al. (2020) · 2020
Later among the works it cites.
GraphACT: Accelerating GCN training on CPU-FPGA heterogeneous platforms
Zeng, H. and Prasanna, V. (2020) · 2020
Later among the works it cites.
Fast convolutional neural networks on FPGAs with hls4ml
Aarrestad, T. et al. (2021) · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pileup mitigation at the Large Hadron Collider with graph neural networks
Arjona Martínez, J., Cerri, O., Pierini, M., Spiropulu, M., and Vlimant, J.-R. (2019) · 2019
Cited alongside, same era.
google/QKeras (San Francisco, CA, USA: GitHub)
Coelho, C. et al. (2019) · 2019
Cited alongside, same era.
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Qasim, S. R., Kieseler, J., Iiyama, Y., and Pierini, M. (2019) · 2019
Cited alongside, same era.
The tracking machine learning challenge: Accuracy phase
Amrouche, S., Basara, L., Calafiura, P., Estrade, V., Farrell, S., Ferreira, D. R., et al. (2020) · 2020
Cited alongside, same era.
Operation of the ATLAS trigger system in Run 2
ATLAS Collaboration (2020) · 2020
Cited alongside, same era.
Hardware acceleration of graph neural networks
Auten, A., Tomei, M., and Kumar, R. (2020) · 2020
Cited alongside, same era.
The Phase-2 upgrade of the CMS Level-1 trigger
CMS Collaboration (2020b) · 2020
Cited alongside, same era.
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Coelho, C. N., Kuusela, A., Li, S., Zhuang, H., Ngadiuba, J., Aarrestad, T. K., et al. (2021) · 2021
Closest in time.
Charged particle tracking via edge-classifying interaction networks
DeZoort, G., Thais, S., Ojalvo, I., Elmer, P., Razavimaleki, V., Duarte, J., et al. (2021) · 2021
Closest in time.
abdelabd/hls4ml: v0.6.0-pyg (Geneva, Switzerland: Zenodo)
Elabd, A. et al. (2021) · 2021
Closest in time.
Govorkova, E., Puljak, E., Aarrestad, T., James, T., Loncar, V., Pierini, M., et al. (2021) · 2021
Closest in time.
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
Hawks, B., Duarte, J., Fraser, N. J., Pappalardo, A., Tran, N., and Umuroglu, Y. (2021) · 2021
Closest in time.
Performance of a geometric deep learning pipeline for HL-LHC particle tracking
Ju, X. et al. (2021) · 2021
Closest in time.
Semi-supervised graph neural network for particle-level noise removal
Li, T., Liu, S., Feng, Y., Tran, N., Liu, M., and Li, P. (2021) · 2021
Closest in time.
fastmachinelearning/hls4ml: coris (v0.6.0) (Geneva, Switzerland: Zenodo)
Loncar, V., Summers, S., Duarte, J., Tran, N., Kreis, B., Ngadiuba, J., et al. (2021) · 2021
Closest in time.
Xilinx/brevitas: v0.7.1 (Geneva, Switzerland: Zenodo)
Pappalardo, A. et al. (2021) · 2021
Closest in time.
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
Pata, J., Duarte, J., Vlimant, J.-R., Pierini, M., and Spiropulu, M. (2021) · 2021
Closest in time.
Vivado Design Suite User Guide: High Level Synthesis
Xilinx, Inc. (2020) · 2021
Closest in time.
UltraScale+ FPGAs product tables and product selection guide
Xilinx, Inc. (2021) · 2021
Closest in time.
Graph neural networks for particle tracking and reconstruction
Duarte, J. and Vlimant, J.-R. (2022) · 2022
Closest in time.