Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs

Frady, E. Paxon; Orchard, Garrick; Florey, David; Imam, Nabil; Liu, Ruokun; Mishra, Joyesh; Tse, Jonathan; Wild, Andreas; Sommer, Friedrich T.; Davies, Martin

doi:10.48550/arxiv.2004.12691

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“…Neuromorphic processors are seriously different from classical solutions and are still at the research stage. But the available results carried out in the framework of open research programs of Intel and other companies demonstrate the superiority of such neuromorphic systems in solving classification problems both in response time and in energy efficiency [59].…”

Section: Neuromorphic Processorsmentioning

confidence: 99%

Cellular automaton based track finder in the STAR experiment (BNL, USA)

Kozlov¹

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The relevant field of interest in High Energy Physics experiments is shifting to searching and studying extremely rare particles and phenomena. The search for rare probes requires an increase in the number of available statistics by increasing the particle interaction rate. The structure of the events also becomes more complicated, the multiplicity of particles in each event increases, and a pileup appears. Due to technical limitations, such data flow becomes impossible to store fully on available storage devices. The solution to the problem is the correct triggering of events and real-time data processing. In this work, the issue of accelerating and improving the algorithms for reconstruction of the charged particles' trajectories based on the Cellular Automaton in the STAR experiment is considered to implement them for track reconstruction in real-time within the High-Level Trigger. This is an important step in the preparation of the CBM experiment as part of the FAIR Phase-0 program. The study of online data processing methods in real conditions at similar interaction energies allows us to study this process and determine the possible weaknesses of the approach. Two versions of the Cellular Automaton based track reconstruction are discussed, which are used, depending on the detecting systems' features. HFT~CA Track Finder, similar to the tracking algorithm of the CBM experiment, has been accelerated by several hundred times, using both algorithm optimization and data-level parallelism. TPC~CA Track Finder has been upgraded to improve the reconstruction quality while maintaining high calculation speed. The algorithm was tuned to work with the new iTPC geometry and provided an additional module for very low momentum track reconstruction. The improved track reconstruction algorithm for the TPC detector in the STAR experiment was included in the HLT reconstruction chain and successfully tested in the express production for the online real data analysis. This made it possible to obtain important physical results during the experiment runtime without the full offline data processing. The tracker is also being prepared for integration into a standard offline data processing chain, after which it will become the basic track search algorithm in the STAR experiment.

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Section: Neuromorphic Processorsmentioning

confidence: 99%

Cellular automaton based track finder in the STAR experiment (BNL, USA)

Kozlov¹

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Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs

Cited by 1 publication

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Cellular automaton based track finder in the STAR experiment (BNL, USA)

Cellular automaton based track finder in the STAR experiment (BNL, USA)

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