1. 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

2. 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) Point 1
Surely we can parallelize our way out of trouble?

3. 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?

4. Computing with simple elements
Neural Networks:
Computing with simple elements
Simple computing elements…
‘neuron’
by themselves, limited functional repertoire.
(Kristofer Bouchard, LBNL)

5. Feed-forward NN: Classification
Simple computing elements…
‘neuron’
as a network, learn to perform diverse functions
Flow of information 1 2
Classification

6. Convolutional NN: Feature Extraction
Simple computing elements…
‘neuron’
as a network, learn to perform diverse functions 60
Feature Extraction
Classification

7. Recurrent NN: Time-varying Functions
Simple computing elements…
‘neuron’
as a network, learn to perform diverse functions
Dynamics
F(t)
Feature Extraction
Classification
Time-varying Functions

8. 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

9. LHCb Trigger Retina Processor
Track parameter space
22K bins, one “receptor” per bin
FPGA implementation
1mus tracking
Offline-quality performance
Certainly good enough for seeding

10. Neuromorphic Computing
“Spikey” from Electronic Visions group in Heidelberg
Qualcomm’s NPU’s for robots.
IBM’s TrueNorth
Stanford’s Neurogrid
(Peter’s comments about each of these chips)
In the traditional von Neumann architecture, a powerful logic core (or several in parallel) operates sequentially on data fetched from memory. In contrast, "neuromorphic" computing distributes both computation and memory among an enormous number of relatively primitive "neurons," each communicating with hundreds or thousands of other neurons through "synapses." – Don Monroe (ACM Communications). These can be considered brain “inspired” chips, with some in a few years approaching 1% of the human brain. The designs are all over the place, with a majority employing spiking neural networks, with a split between stochastic and deterministic designs with some even employing analog circuits. The next slides will describe two main classes of supervised NN algorithms that will be the focus of our research: feed forward neural network and recurrent neural network
SpiNNaker’s 1B neuron machine
Intel’s concept design...
(Peter Nugent, LBNL) 10

11. 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) 11

12. 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

13. Deep Learning for Data Analysis
Peter Sadowski (UCI)

14. Optimizing Higgs Detection
Peter Sadowski (UCI)

15. Optimizing Higgs Detection
HiggsML winner used DNN
Peter Sadowski (UCI)

16. dianahep (NSF S2I2 project) Improve ML tools in ROOT

17. 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

18. 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

19. Thanks Kristofer Bouchard David Clark Kyle Cranmer
Maurice Garcia-Sciveres
Peter Nugent
Peter Sadowski
Tracking Kaggle group … ]

20. Backup

21. Possible Tracking Network
Dynamics
Candidates
Track
F(t)
Seeding
Selection
Fitting

22. Kalman Filters and Recurrent NNs
Classic Data Assimilation algorithm (1960, NASA)
Iteratively track evolution of a dynamic system
State
Data
Kalman
Filter
Dynamics
Data
Dyn
State
Recurrent
Neural
Network
Saturn V navigation
Autopilots
GPS dead-reckoning obstacle downtown city