us atlas sctpm machine learning and atlas paolo calafiura what is ml: data-driven statistical...
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US ATLAS SCTPM
Machine Learning and ATLAS
Paolo Calafiura
What is ML: data-driven statistical modeling of complex systems
Why now: GPU/MIC/FPGA/Neuromorphic hardware evolution pushes us towards computing with many simple elements
Applications: many! Today will focus on
• Neuromorphic computing for low-power scalable tracking
• Deep Neural Networks for data analysis, data movement
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Motivating Example: Tracking
During Run 4 we have to process ~60M tracks/s (~20x Run 2)
I/O will likely constrain us to run full tracking on line, and to write only an xAOD-like format
Guesstimating x86 cost & performance evolution, we should find x2-5 CPU offline, (much) more @ Point 1
Surely we can parallelize our way out of trouble?
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Why is it so hard to do particle tracking in parallel?
Algorithms: Iterative (propagation, fitting), irregular (combinatorial searches with lots of branch points)
Data: sparse (hits), non-local access (B-field integration)
Can ML allow us to train Neural Networks (NN) that use regular, trivial algorithms, and a naturally data parallel approach?
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Neural Networks:Computing with simple elements
‘neuron’
by themselves, limited functional repertoire.
Simple computing elements…
(Kristofer Bouchard, LBNL)
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Feed-forward NN: Classification
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‘neuron’
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as a network, learn to perform diverse functions
Simple computing elements…
Flow of information
Classification
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Convolutional NN: Feature Extraction
Feature Extraction Classification
‘neuron’
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Simple computing elements…
as a network, learn to perform diverse functions
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Recurrent NN: Time-varying Functions
F(t)
‘neuron’ Simple computing elements…
Feature Extraction Classification Time-varying Functions
Dynamics
as a network, learn to perform diverse functions
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Tracking Kaggle Challenge
Inspired by the very successful Higgs Challenge
Competition among ML experts:
• Problem: Given a list of space-points produce a list of track candidates
• Figure of merit: efficiency for given fake rate and CPU budget (still under discussion)
More next week during C&S plenary
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LHCb Trigger Retina Processor
FPGA implementation1mus trackingOffline-quality performanceCertainly good enough for seeding
Track parameter space22K bins, one “receptor” per bin
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Neuromorphic Computing
“Spikey” from Electronic Visions group in Heidelberg
Qualcomm’s NPU’s for robots.
SpiNNaker’s 1B neuron machine
Stanford’s Neurogrid
Intel’s concept design...
IBM’s TrueNorth
(Peter Nugent, LBNL)
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IBM TrueNorth
• 1 million programmable neurons• 256 million synapses• 4096 neurosynaptic cores• Uses 70mW per chip• 5.4 billion transistors• Spiking rate >1000Hz
A single chip can process color video in real-time while consuming 176,000 times less energy than a current Intel chip performing the exact same analysis. Note the Intel chip can not do this analysis in real-time and is in fact 300 times slower! Merolla+ Science (2014)
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Neuromorphic Kalman Filters (LBNL LDRD FY16 proposal)
Paolo Calafiura (CRD), Kristofer Bouchard (Life Sciences),David Donofrio (CRD), Rebecca Carney (Physics),
Maurice Garcia-Sciveres (Physics), Craig E. Tull (CRD)
Implement Kalman filters on neuromorphic chips for low-power, high-throughput, real-time data processing
Brain-machine interfaces Charged particle tracking
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Deep Learning for Data Analysis
Peter Sadowski (UCI)
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Optimizing Higgs Detection
Peter Sadowski (UCI)
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Optimizing Higgs Detection
Peter Sadowski (UCI)
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dianahep (NSF S2I2 project) Improve ML tools in ROOT
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Machine Learning and Data Management
Vast and growing amount of data on user access patterns. Combine engineered and learned features to:
• Pre-fetch data and pre-allocate resources
• Optimize data clustering and replication
• Suggest related data sets
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Conclusions
• ML will be predominant in Run 4 analysis (wager #1)• Deep neural networks in tracking, jet reco, and clustering will
allow us to exploit GPUs and FPGAs, and possible neuromorphic architectures (wager #2)
• 2025 grad-students will wonder why we wrote all that C++ junk instead of training a few good networks (wager #3)
ML learning experts are not formed overnight (and command high 6-digits salaries, so tend to disappear fast)
US ATLAS should start developing ML expertise by supporting pilot projects in all relevant areas, e.g. data analysis, reconstruction (tracking), and data/job management
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Thanks
• Kristofer Bouchard• David Clark• Kyle Cranmer• Maurice Garcia-Sciveres• Peter Nugent• Peter Sadowski• Tracking Kaggle group• …
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Backup
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Possible Tracking Network
F(t)
Seeding Selection Fitting
Dynamics
Track
Candidates
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Kalman Filters and Recurrent NNs
Classic Data Assimilation algorithm (1960, NASA)Iteratively track evolution of a dynamic system
StateData
KalmanFilter
Dynamics
Data
Dyn
State
RecurrentNeuralNetwork