1. 1 Status of Online Neural Networks Bruce Denby Université de Versailles and Laboratoire des Instruments et Systèmes, Paris, France Rapporteur’s Presentation ACAT2000 Fermilab 16-20 October, 2000

2. 2 I.The current situation II.Developments foreseen III.Neural net hardware IV.Conclusions OUTLINE OF THE PRESENTATION

3. 3 Acknowledgements Most of my transparencies were borrowed from the talks of: Sotirios Vlachos Erez Etzion Jean-Christophe Prévotet Christian Kiesling Bertrand Granado

4. 4 The Current Situation Neural network triggers are being used to produce physics. Examples: 1) Dirac Experiment at the CERN PS 2) H1 Experiment at HERA

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6. 6 34 GeV p on target Measure lifetime of pionium Hodoscope input to NN

7. 7 -The network is trained to select low Q events

8. 8 Net architecture 55-2-1 Note that the multiply/accumulate and sigmoid evaluation are done using look-up table memories.

9. 9 - i.e., it works….

10. 10 The H1 Neural Network Trigger Project Christian Kiesling Max-Planck-Institut für Physik München, Germany

11. 11 p 920 GeV 27.6 GeV e The H1 Experiment at HERA Mission: u u d Gluon Hardware (MPI): Liquid Argon Calorimeter (forward barrel section) LAr front end electronics LAr trigger (L1) Neural Network Trigger (L2) Physics analysis : Measurements of the structure functions F 2, F L, F 3, F 2 D Jet Measurements (strong coupling constant) Charm/Beauty Production (gluon content of proton) Diffractive Vector Meson Production (gluon struct.) Search for Instanton Effects (QCD „exotics“) study the structure of the nucleon the fundamental interactions of quarks and gluons : Quantum chromodynamics (QCD) electroweak interference search for physics beyond the Standard Model

12. 12 The H1 Trigger Scheme hardware software L1 trigger: OR of individual subdetector triggers, such as MWPC, CJC, LAr, SpaCal,  system... Neural Network at Level 2: Global Event Decision L2 systems: have access to information from all subdetectors (information prepared by subtrigger processors)

13. 13 “physics” “background” MWPC’s (2 sets) z-VRTX (from MWPC) Trigger towers, global energies (8 bit numbers) Trigger towers above threshold (single bits) Hits (single bits) Nr. of tracks (8 bit numbers) 16 bin histogram (8bit numbers) Detector Information at level 2 (example of photoproduction) and there is much more physics in H1... Calorimeter (LAr) hadronic electromagnetic Central Jet chamber SpaCal (Pb scint.) µ chambers

14. 14 Output (only one neuron) Three-Layer Feed Forward Neural Net weights Architecture of the H1 Neural Network Trigger background One hidden layer Inputs (from detector) physics discriminate „physics“ from „background“ : Central Problem: Inputs for the Neural Nets Data Selection Data Transformation

15. 15 Organization and Processing of Data from L1 Subdetector information arrives in consecutive time slices t i („frames“, or bunch crossings BC) (t max = 32 BC’s at present) 1 BC = 96 ns = 10 MHz transfer rate 0 2 4 6 8... t(BC) 0123456701234567 Subdetector 1 Subdetector 2 Subdetector 3 DDBIDDBI DDBIDDBI to neural network L2 crate backplane: L2 Bus The L2 Bus (8 subbusses, 16 bit wide) The Data Distribution Board (preprocessing of neural input) Cables from subdetectors (maximum of 40) data input units: Selection of input data Processing (look-up, summing)

16. 16 CNAPS 0 CNAPS 1 CNAPS 2 CNAPS 3 CNAPS 4 CNAPS 5 CNAPS 6 CNAPS 7 CNAPS 8CNAPS 9 CNAPS 10CNAPS 11 VME SUN / SBus Interface Monitoring DDB 0DDB 1DDB 2DDB 3DDB 4DDB 5 DDB 6DDB 7 DDB 8DDB 9DDB 10DDB 11 SBus Interface Data from Detector To Final Decider X11 Terminal Loading and Control The Neural Trigger System Set of independent networks, each one trained for a specific physics reaction Network processors Data selection and Data transformation : Modular and Expandable

17. 17 The complete System 12 independent networks Pre-processing modules (one for each neural network) Cables carrying raw input data from the detector Total of 1024 processors Integrated computing power: over 20 Giga MAC/sec

18. 18 (random day in early 1999) Trigger rate Monitor (24h)The Neural Network Trigger in Operation: 1: 2: 4: 5: 6: 7: 8: 9: 10: 11: (Boxes 0 and 3 also active during 99/00) Background rejection factor > 100 !

19. 19 Some Physics: Elastic Photoproduction of Mesons expected large in QCD expected small in Regge theory Due to highly selective NN trigger background is under control up to the highest HERA energies QCD xgxg C. Adloff et al., Phys. Lett. B483 (2000) 23

20. 20 Photoproduction of  Mesons with Proton Dissociation Recent results on d  /dt : Measurement possible due to neural trigger (publication in preparation)

21. 21 Developments Foreseen I.H1 upgrade II.Atlas

22. 22 Why a new preprocessor? Neural Network Trigger successfully in operation since Summer 1996, promising physics results, but: NOW:need to prepare for higher selectivity (luminosity upgrade: HERA 2000: factor 5 more physics @ constant logging rate) New Goal: separate “interesting” physics from “uninteresting”physics Need more Intelligent Preprocessing H1: New Network Preprocessing - The DDB II So far no information from LAr trigger towers used, only global energy sums, no subdetector correlations (limitation was dictated by time schedule for the realization of the trigger)

23. 23 Intelligent Preprocessing for Neural Networks Jean-Christophe Prévotet, MPI München Laboratoire des Instruments et Systèmes (Paris VI)

24. 24 New Preprocessing : The DDB2 Principle - “intelligent” preprocessing” extract physical values for the neural net (impulse, energy, particle type) - Combination of information from different subdetectors (the,phi plane) - Executed in 4 steps Clustering Matching Ordering Post Processing find regions of interest within a given detector layer combination of clusters belonging to the same object sorting of objects by parameter generates variables for the neural network

25. 25 Description of a DDB2 board L2 bus MatchingOrdering Post Processing Clustering BT/TT Clustering MWPC Clustering CJC Clustering FTT Clustering Muon Clustering Spacal Workable data given to the NN MEM Data Addresses Storage of parameters Addresses Matching

26. 26 Hardware specifications Each board works on the same data but parameterized differently Organization : 5 DDB2 boards connected to 5 CNAPS Re-configurable hardware independent of data format changes Time : 8µs (Clustering, Matching, Ordering, Post Processing)

27. 27 Hardware resources Time: 8 µs Parallel processing Pipeline steps FPGA: - Low cost (prototype board) - Speed - Xilinx Virtex Family XCV200, XCV400 XCV200 236K 1475K XCV400 468K 20153K Data format Luts Lot of small memories TypeN° gates RamsSelRam bits ClusteringMatching Ordering Post processing 6 to 8 XCV2002 XCV4001 to ? XCV200 Algorithm Number Type

28. 28 Gain about a factor of 2 in efficiency with the new DDB II algorithms for this case. Expect increased selectivity also for other physics... How does Physics profit from the DDB II ? PhysicsBackgr. DDB I Backgr. Physics „DDB II“ (DDB II simulated with DDB I)  photo- production Test reaction:  photo-production

29. 29 Momentum Reconstruction and Triggering in the ATLAS Detector FermiLab, October 2000 Erez Etzion 1, Gideon Dror 2, David Horn 1, Halina Abramowicz 1 1. Tel-Aviv University, Tel Aviv, Israel. 2. The Academic College of Tel-Aviv-Yaffo, Tel Aviv, Israel.

30. 30 ATLAS S.C Solenoid Hadron Calorimeter Muon Detectors EM Calorimeters Inner Detector S.C Air core Toroids

31. 31 LowPt High Pt trigger Complicated magnetic field map => difficult problem

32. 32 Network architecture PTPT Q   sigmoid hidden layers linear output input parameters of straight track of muon(preprocessing LMS)

33. 33 Testing network performance Training set 2500 events. In one octant. Test set of 1829 events. Distribution of network errors - approximately gaussian. compatible with stochasticity of the data. charge is discrete!!! 95.8% correct sign.

34. 34 Summary & discussion The network can successfully estimate the charge and transverse momentum of the muon. Classification (triggering) is most efficient by specially trained network. The data is intrinsically stochastic giving rise to approximately gaussian errors. The simplicity of the network enables very fast hardware realization. (See presentation this workshop)

35. 35 Neural Network Hardware Off-the-shelf neural net hardware is scarce Many standard products no longer exist What should we do in HEP?

36. 36 ETANN, 1991 (Electrically Trainable Artificial Neural Network by Intel) (64x64x4 in 5  s) CNAPS 1993 (Adaptive Solutions, Oregon) 64 @20 MHz 8/16 „Silicon Brain“ (Irvine Sensors Inc.) 3D analog FPGA array NeuroClassifier, 1994 (by P. Masa, Univ. Twente, NL)(70x6x1 in 20 ns) SAND1 1995 (KfK, Germany) 4 @50 MHz16/16 recent development: Maharadja, 1999 (Paris, France)details at this conference (see talk of B. Granado, AI, Sess.I) back to analog (?) Analog Devices: Digital Devices: MA16 1994 (Siemens, Germany) 16 @50 MHz16/16 TOTEM 1994 (Trento, Italy) 32 @30 MHz16/ 8 towards a complexity similar to the human brain... Blue color: chip no longer produced

37. 37 - One interesting solution: use memories to evaluate NN’s

38. 38 - Another solution: can we use a fast ‘general purpose’ NN processor implemented in FPGA’s?

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45. 45 - FPGA clock speed of 100 MHz will be available soon. - implying execution times of a few 100 ns.

46. 46 Conclusions Fast preprocessing is a concern – FPGA’s are one way to go H1 NN trigger upgrade is in the works There is some NN trigger Neural net triggers exist and they work activity in LHC experiments: ATLAS muon proposal (this workshop), CMS (electron trigger, Varela et al.) Finding NN hardware is a problem Memory or FPGA implementations may be the answer See also Neural Networks in High Energy Physics: A Ten Year Perspective, B. Denby, Comp. Phys. Comm. 119, August 1, 1999, p 219.