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Neuromorphic Kalman filter implementation in IBM's TrueNorth
Lawrence Berkeley National Laboratory, USA.ORCID iD: 000-0001-5659-4440
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2017 (English)In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 898, no 4, article id 042021Article in journal (Refereed) Published
Abstract [en]

Following the advent of a post-Moore’s law field of computation, novel architectures continue to emerge. With composite, multi-million connection neuromorphic chips like IBM’s TrueNorth, neural engineering has now become a feasible technology in this novel computing paradigm. High Energy Physics experiments are continuously exploring new methods of computation and data handling, including neuromorphic, to support the growing challenges of the field and be prepared for future commodity computing trends. This work details the first instance of a Kalman filter implementation in IBM’s neuromorphic architecture, TrueNorth, for both parallel and serial spike trains. The implementation is tested on multiple simulated systems and its performance is evaluated with respect to an equivalent non-spiking Kalman filter. The limits of the implementation are explored whilst varying the size of weight and threshold registers, the number of spikes used to encode a state, size of neuron block for spatial encoding, and neuron potential reset schemes.

Place, publisher, year, edition, pages
2017. Vol. 898, no 4, article id 042021
National Category
Accelerator Physics and Instrumentation Subatomic Physics
Research subject
Physics
Identifiers
URN: urn:nbn:se:su:diva-168226DOI: 10.1088/1742-6596/898/4/042021OAI: oai:DiVA.org:su-168226DiVA, id: diva2:1307119
Conference
22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP, San Francisco, USA, October 10-14, 2016
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-05-03Bibliographically approved
In thesis
1. Silicon Tracking and a Search for Long-lived Particles
Open this publication in new window or tab >>Silicon Tracking and a Search for Long-lived Particles
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The ATLAS Detector, below the surface of the Swiss-French border, measures the remnants of high-energy proton-proton collisions, accelerated by the Large Hadron Collider (LHC) at CERN. Recently the LHC paused operations, having delivered an integrated luminosity corresponding to 150 fb−1 of data at a centre-of-mass energy of 13 TeV. This thesis describes a search for physics beyond the Standard Model using that dataset as well as the charged particle tracking detector technology that renders it possible. The analysis searches for long-lived, massive particles identified by a characteristic decay displaced from the interaction point and produced in association with high momentum jets.

Searching for rare processes requires sifting through a large amount of data, which stresses the ATLAS computing infrastructure. As such, measures are taken to reduce unnecessary computations and supplement our existing resources with, for example, inherently parallel computing architectures. Early adoption of these new architectures is necessary to understand the feasibility of their potential integration, including porting existing algorithms. A popular algorithm used in track reconstruction, the Kalman filter, has been implemented in a neuromorphic architecture: IBM’s TrueNorth. The limits of using such an architecture for tracking, as well as how its performance compares to a non-spiking Kalman filter implementation, are explored in this thesis.

In 2026 the LHC will enter a High Luminosity phase (HL-LHC), increasing the instantaneous luminosity by a factor of five and delivering 4000 fb-1 within twelve years. This will impose significant technical challenges on all aspects of the ATLAS detector, resulting in the entire ATLAS Inner Detector being replaced by an all-silicon tracker. ITk (the new “Inner TracKer”) will be comprised of Strip and Pixel detectors. The layout of the Pixel and Strip detectors was optimised for the upgrade to extend their forward coverage. To cope with the increased number of hits per chip per event and explore novel techniques for dealing with the conditions in HL-LHC, an inter-experiment collaboration, RD53, was formed, tasked with producing a front-end readout chip used in Pixel detectors. This thesis will briefly outline the author’s contribution to both of these projects.

ITk silicon sensors will undergo significant damage over their lifetime due to non-ionising energy loss (NIEL). This damage must be incorporated into the detector simulation both to predict the detector performance and to understand the effects of radiation damage on data taking. The implementation of NIEL radiation damage in the ATLAS simulation framework is discussed in this thesis.

Place, publisher, year, edition, pages
Stockholm: Department of Physics, Stockholm University, 2019. p. 208
Keywords
ATLAS, silicon, silicon tracking, radiation damage, neuromorphic, neuromorphic computing, long-lived particles, susy, rpvll, displaced vertices, pixel, pixel detector
National Category
Subatomic Physics
Research subject
Physics
Identifiers
urn:nbn:se:su:diva-168230 (URN)978-91-7797-733-9 (ISBN)978-91-7797-734-6 (ISBN)
Public defence
2019-06-13, sal FB42 AlbaNova universitetscentrum, Roslagstullsbacken 21, Stockholm, 09:00 (English)
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Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.

 

Available from: 2019-05-21 Created: 2019-04-26 Last updated: 2019-05-20Bibliographically approved

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