Applications and Techniques for Fast Machine Learning in Science

Deiana, A. M.; Tran, N. V.; Agar, Joshua C.; Blott, Michaela; Guglielmo, Giuseppe Di; Duarte, J.; Harris, P.; Hauck, Scott; Liu, Mia; Neubauer, M. S.; Ngadiuba, J.; Ogrenci-Memik, Seda; Pierini, M.; Aarrestad, Thea Klaeboe; Bähr, Steffen; Becker, Jürgen; Berthold, A.; Bonventre, Richard; Bravo, Tomas E. Muller; Diefenthaler, M.; Dong, Zhen; Fritzsche, N.; Gholami, Amir; Говоркова, Е.; Hazelwood, Kyle; Herwig, T. C.; Khan, Babar; Kim, Sehoon; Klijnsma, T.; Liu, Yaling; Lo, Kin Ho; Nguyen, T.; Pezzullo, Gianantonio; Rasoulinezhad, S A; Rivera, R.; Scholberg, K.; Justin, Selig,; Sen, Sougata; Strukov, Dmitri B.; Tang, W. M.; Thais, S. J.; Unger, K.; Vilalta, Ricardo; Krosigk, B. von; Warburton, Thomas; Flechas, Maria Acosta; Aportela, Anthony; Calvet, T. P.; Cristella, L.; Díaz, D.; Doglioni, C.; Galati, Maria Domenica; Khoda, E. E.; Fahim, Farah; Giri, Davide; Hawks, Benjamin; Hoang, Duc; Holzman, B.; Hsu, S.‐C.; Jindariani, S.; Johnson, Iris; Kansal, Raghav; Kastner, Ryan; Katsavounidis, E.; Krupa, J.; Pan, Li; Madireddy, Sandeep; Marx, E. J.; McCormack, Patrick; Meza, Andrés; Mitrevski, J.; Attia, Mohammed, Mohammed; Mokhtar, F.; Moreno, Eric A.; Nagu, S.; Narayan, R.; Palladino, N.; Que, Zhiqiang; Park, Sang Eon; Ramamoorthy, Subramanian; Rankin, D.; Rothman, Simon; Sharma, Ashish; Summers, S.; Vischia, P.; Vlimant, J. R.; Weng, Olivia

doi:10.3389/fdata.2022.787421

Cited by 35 publications

(14 citation statements)

References 502 publications

(560 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a more detailed review of these ML techniques, please see Ref. [29] and references therein written by Amir Gholami, Zhen Dong, and Sehoon Kim -what follows here is a very reduced summary.…”

Section: Reduced Precision and Compressionmentioning

confidence: 99%

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Carini¹,

Deptuch²,

Dickinson³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for nearsensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.

show abstract

Section: Reduced Precision and Compressionmentioning

confidence: 99%

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Carini¹,

Deptuch²,

Dickinson³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Development of ML algorithms for use in the trigger and data acquisition systems of future HEP experiments is particularly challenging [24]. For example, while ML-based algorithms have proven effective at performing data reduction, through both advanced data selection and data compression, to be used in high-luminosity environments of future particle colliders these algorithms must be capable of running in on-detector electronics with latencies on the order of nanoseconds.…”

Section: Heterogeneous Computing and Machine Learningmentioning

confidence: 99%

Report of the Instrumentation Frontier Working Group for Snowmass 2021

Barbeau¹

2022

Preprint

View full text Add to dashboard Cite

Detector instrumentation is at the heart of scientific discoveries. Cutting edge technologies enable US particle physics to play a leading role worldwide. This report summarizes the current status of instrumentation for High Energy Physics (HEP), the challenges and needs of future experiments and indicates high priority research areas. The Instrumentation Frontier studies detector technologies and Research and Development (R&D) needed for future experiments in collider physics, neutrino physics, rare and precision physics and at the cosmic frontier. It is divided into more or less diagonal areas with some overlap among a few of them. We lay out five high-level key messages that are geared towards ensuring the health and competitiveness of the US detector instrumentation community, and thus the entire particle physics landscape.

show abstract

“…Recently, several different machine learning methods have been implemented using field-programmable gate array (FPGA) devices for real-time applications in high energy physics [3][4][5][6][7][8][9][10]. The current state of applications and techniques for fast machine learning is summarised in the recently released community report [11].…”

Section: Introductionmentioning

confidence: 99%

Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

et al. 2022

View full text Add to dashboard Cite

This paper reports on the development of a resource-efficient FPGA-based neural network regression model for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned ATLAS upgrades for the High Luminosity LHC, an entirely new FPGA-based hardware muon trigger system will be installed that will process full muon detector data within a 10 $${\upmu }\text {s}$$ μ s latency window. The large FPGA devices planned for this upgrade should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our neural network regression model promises to improve rejection of the dominant source of background trigger events in the central detector region, which are due to muon candidates with low transverse momenta. This model was implemented in FPGA using 157 digital signal processors and about 5000 lookup tables. The simulated network latency and deadtime are 122 and 25 ns, respectively, when implemented in the FPGA device using a 320 MHz clock frequency. Two other FPGA implementations were also developed to study the impact of design choices on resource utilisation and latency. The performance parameters of our FPGA implementation are well within the requirements of the future muon trigger system, therefore opening a possibility for deploying machine learning methods for future data taking by the ATLAS experiment.

show abstract

Applications and Techniques for Fast Machine Learning in Science

Cited by 35 publications

References 502 publications

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Report of the Instrumentation Frontier Working Group for Snowmass 2021

Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

Contact Info

Product

Resources

About