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Status of Online Neural Networks

Bruce DenbyUniversité de Versailles and

Laboratoire des Instruments et Systèmes, Paris, France

Rapporteur’s Presentation

ACAT2000 Fermilab 16-20 October, 2000

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I. The current situationII. Developments foreseenIII. Neural net hardwareIV. Conclusions

OUTLINE OF THE PRESENTATION

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Acknowledgements

Most of my transparencies were borrowed from the talks of:

• Sotirios Vlachos• Erez Etzion• Jean-Christophe Prévotet• Christian Kiesling• Bertrand Granado

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The Current Situation

Neural network triggers are being used to produce physics.

Examples: 1) Dirac Experiment at the CERN PS2) H1 Experiment at HERA

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

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-The network is trained to select low Q events

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

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- i.e., it works….

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The H1 Neural Network Trigger Project The H1 Neural Network Trigger Project

Christian Kiesling Max-Planck-Institut für Physik

München, Germany

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p

920 GeV

27.6 GeV

e

The H1 Experiment at HERA

Mission:

u

u

dGluon

Hardware (MPI):Liquid Argon Calorimeter (forward barrel section)LAr front end electronicsLAr trigger (L1)Neural Network Trigger (L2)

Physics analysis :Measurements of the structure functions F2, FL, F3, F2

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

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The H1 Trigger Scheme

hard

ware

soft

ware

L1 trigger: OR of individual subdetectortriggers, such asMWPC, CJC, LAr, SpaCal, system ...

Neural Network at Level 2:

Global Event Decision

L2 systems: have access to informationfrom all subdetectors(information prepared bysubtrigger processors)

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“physics”

“background”

MWPC’s (2 sets)

z-VRTX (from MWPC)

Trigger towers,global energies(8 bit numbers)

Trigger towersabove threshold(single bits)

Hits (single bits)

Hits (single bits)

Nr. of tracks(8 bit numbers)

16 bin histogram(8bit numbers)

Detector Informationat level 2

(example of photoproduction)/J

and there is much more physics in H1 ...

Calorimeter (LAr)hadronic electromagnetic

Central Jet chamber

SpaCal (Pb scint.)

µ chambers

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Output (only one neuron)

Three-Layer Feed Forward Neural Net

),(0 ijwxFy weights

Architecture of the H1 Neural Network Trigger

00 y background

One hidden layer

Inputs (from detector)

10 y physics

discriminate„physics“ from „background“ :

Central Problem:Inputs for the Neural Nets

Data Selection

Data Transformation

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Organization and Processing of Data from L1

Subdetector information arrives in consecutive time slices ti („frames“, orbunch crossings BC)

(tmax = 32 BC’s at present)

1 BC = 96 ns = 10 MHz transfer rate0 2 4 6 8 ...t(BC)

01234567

Subdetector 1

Subdetector 2

Subdetector 3

DDBI

DDBI

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)

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CN

AP

S 0

CN

AP

S 1

CN

AP

S 2

CN

AP

S 3

CN

AP

S 4

CN

AP

S 5

CN

AP

S 6

CN

AP

S 7

CN

AP

S 8

CN

AP

S 9

CN

AP

S 1

0

CN

AP

S 1

1

VM

E S

UN

/S

Bus

Int

erfa

ceM

onit

orin

g

DD

B 0

DD

B 1

DD

B 2

DD

B 3

DD

B 4

DD

B 5

DD

B 6

DD

B 7

DD

B 8

DD

B 9

DD

B 1

0

DD

B 1

1

SB

us I

nter

face

Data from DetectorTo Final Decider

X11Terminal

Loading and Control

The Neural Trigger System

Set of independentnetworks,each one trained for a specific physics reaction

Network processors

Data selection andData transformation

: Modular and Expandable

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The complete System

12 independent networks

Pre-processing modules (one for eachneural network)

Cables carrying rawinput data from the detector

Total of 1024 processors

Integrated computingpower:over 20 Giga MAC/sec

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(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:

diff)( XKKp

peJp ),(/

peeJp )(/

peeJp )(/

peeJp )(/

XKDepe )(*

XDp *

Xp

VM

XJp )(/

(Boxes 0 and 3 also active during 99/00)

Backgroundrejection factor > 100 !

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Some Physics: Elastic Photoproduction of Mesons /J

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2

2

222

~,~

),()()(

Vp

Vg

ggS

MQWM

x

QxgxQW

2

2

2

22),(

1)( W

Q

xQxF

QW

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

xg

C. Adloff et al., Phys. Lett. B483 (2000) 23

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Photoproduction of Mesons withProton Dissociation

Recent results on d/dt :

Measurement possible due toneural trigger

(publication in preparation)

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Developments Foreseen

I. H1 upgradeII. Atlas

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Why a new preprocessor?

Neural Network Trigger successfully in operationsince 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)

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Intelligent Preprocessing for Neural NetworksJean-Christophe Prévotet,

MPI MünchenLaboratoire des Instruments et Systèmes (Paris VI)

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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 OrderingPost

Processing

find regions of interest

within a given detector layer

combination of clustersbelonging to the same

object

sorting of objectsby parameter

generatesvariablesfor the

neural network

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Description of a DDB2 boardL2 bus

Matching OrderingPost

Processing

Clustering BT/TT

Clustering MWPC

Clustering CJC

Clustering FTT

Clustering Muon

Clustering Spacal

Workabledata

givento the NN

MEM

MEM

MEM

MEM

MEM

MEM

Data

Addresses

Storageofparameters

Addresses

Matching

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Hardware specifications

Each board works on thesame data but parameterized differently

Organization :5 DDB2 boards connected to 5 CNAPS

Re-configurable hardwareindependent of data format changes

Time : 8µs (Clustering, Matching, Ordering, Post Processing)

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Hardware resources

Time : 8 µs

Parallel processingPipeline steps

FPGA :- Low cost (prototype board)- Speed

- Xilinx Virtex Family XCV200, XCV400

XCV200 236K 14 75K XCV400 468K 20 153K

Data format Luts Lot of small memories

Type N° gates Rams SelRam bits

Clustering Matching Ordering Post processing

6 to 8 XCV200 2 XCV400 1 to ? XCV200 1 to ? XCV200

Algorithm

NumberType

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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

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Momentum Reconstruction and Triggering in the ATLAS Detector

FermiLab, October 2000

Erez Etzion1, Gideon Dror2, David Horn1, Halina Abramowicz1

1. Tel-Aviv University, Tel Aviv, Israel.

2. The Academic College of Tel-Aviv-Yaffo, Tel Aviv, Israel.

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ATLASS.C

SolenoidHadronCalorimeter

Muon DetectorsEM Calorimeters

Inner Detector

S.C Air coreToroids

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LowPt High Pt trigger

Complicated magnetic field map => difficult problem

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Network architecture

PTQ

sigmoid hidden layers

linear output

input

parameters of straight track of muon (preprocessing LMS)

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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.

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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)

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Neural Network Hardware

• Off-the-shelf neural net hardware is scarce• Many standard products no longer exist• What should we do in HEP?

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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 MHz 16/16

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 MHz 16/16

TOTEM 1994 (Trento, Italy) 32 @30 MHz 16/ 8

towards a complexity similarto the human brain ...

Blue color: chip no longer produced

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

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

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

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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.