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The PANDA experiment will not use any hardware trigger, i.e. all raw data are streaming in the data acquisition with a bandwidth of ~280 GB/s. The PANDA Online System is designed to perform data reduction by a factor of ~800 by…

A new generation of experiments is being developed, where the challenge of separating rare signal processes from background at high intensities requires a change of trigger paradigm. At the future PANDA experiment at FAIR, hardware triggers…

Instrumentation and Detectors · Physics 2024-04-08 Jenny Taylor , Michael Papenbrock , Tobias Stockmanns , Ralf Kliemt , Tord Johansson , Adeel Akram , Karin Schönning

Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS). HAS comes with two unique flavors. First, by riding on top of HTTP/TCP, it leverages the…

Networking and Internet Architecture · Computer Science 2016-11-17 Zhi Li , Xiaoqing Zhu , Josh Gahm , Rong Pan , Hao Hu , Ali C. Begen , Dave Oran

A large number of data need to be transmitted in high-speed between Field Programmable Gate Array (FPGA) and Advanced RISC Machines 11 micro-controller (ARM11) when we design a small data acquisition (DAQ) system for nuclear experiments.…

Instrumentation and Detectors · Physics 2013-04-12 Wenxiong Zhou , Yanyu Wang , Gangyang Nan , Jianchuan Zhang

A new $\mu$TCA DAQ system was introduced in CANDLES experiment with SpaceWire-to-GigabitEthernet (SpaceWire-GigabitEthernet) network for data readout and Flash Analog-to-Digital Converters (FADCs). With SpaceWire-GigabitEthernet, we can…

Instrumentation and Detectors · Physics 2019-07-29 B. T. Khai , S. Ajimura , K. Kanagawa , T. Maeda , M. Nomachi , Y. Sugaya , K. Suzuki , M. Tsuzuki

Intensive computation is entering data centers with multiple workloads of deep learning. To balance the compute efficiency, performance, and total cost of ownership (TCO), the use of a field-programmable gate array (FPGA) with…

Computer Vision and Pattern Recognition · Computer Science 2019-09-19 Xiaoyu Yu , Yuwei Wang , Jie Miao , Ephrem Wu , Heng Zhang , Yu Meng , Bo Zhang , Biao Min , Dewei Chen , Jianlin Gao

The upcoming PANDA (anti-Proton ANnihilation at DArmstadt) experiment at FAIR (Facility for Anti-proton and Ion Research) offers unique possibilities for performing hyperon physics such as extraction of spin observables. Due to their…

Nuclear Experiment · Physics 2019-10-15 Jenny Regina , Walter Ikegami Andersson

As the development of electronic science and technology, electronic data acquisition (DAQ) system is more and more widely applied to nuclear physics experiments. Workstations are often utilized for data storage, data display, data…

Instrumentation and Detectors · Physics 2018-06-26 Hongwei Yu , Kezhu Song , Junfeng Yang , Kehan Li , Tengfei Chen , Shiyu Luo , Cheng Tang , Han Yu

In high energy physics experiments (HEP), high speed and fault resilient data communication is needed between detectors/sensors and the host PC. Transient faults can occur in the communication hardware due to various external effects like…

Instrumentation and Detectors · Physics 2015-04-01 Swagata Mandal , Suman Sau , Amlan Chakrabarti , Subhasis Chattopadhyay

The upcoming PandaX-xT experiment will deploy over 3,000 readout channels operating at a 500 MSa/s sampling rate, generating a sustained data bandwidth up to 1.6 GB/s. To meet this demanding requirement, we present AURORA, a…

Instrumentation and Detectors · Physics 2026-04-21 Yihan Guo , Xiaofeng Shang , Chang Cai , Weihao Wu , Xun Chen

Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as…

Hardware Architecture · Computer Science 2017-09-18 Yuan Du , Li Du , Yilei Li , Junjie Su , Mau-Chung Frank Chang

Large LAr TPCs are among the most powerful detectors to address open problems in particle and astro-particle physics, such as CP violation in leptonic sector, neutrino properties and their astrophysical implications, proton decay search…

Instrumentation and Detectors · Physics 2009-08-06 C. Girerd , D. Autiero , B. Carlus , S. Gardien , J. Marteau , W. Tromeur

Among hardware accelerators for deep-learning inference, data flow implementations offer low latency and high throughput capabilities. In these architectures, each neuron is mapped to a dedicated hardware unit, making them well-suited for…

Machine Learning · Computer Science 2026-03-10 Tobias Habermann , Michael Mecik , Zhenyu Wang , César David Vera , Martin Kumm , Mario Garrido

PANDA (anti-Proton ANnihiliation at DArmstadt) is planned to be one of the four main experiments at the future international accelerator complex FAIR (Facility for Antiproton and Ion Research) in Darmstadt, Germany. It is going to address…

Instrumentation and Detectors · Physics 2020-01-01 Panda Collaboration , F. Davi , W. Erni , B. Krusche , M. Steinacher , N. Walford , H. Liu , Z. Liu , B. Liu , X. Shen , C. Wang , J. Zhao , M. Albrecht , T. Erlen , F. Feldbauer , M. Fink , V. Freudenreich , M. Fritsch , F. H. Heinsius , T. Held , T. Holtmann , I. Keshk , H. Koch , B. Kopf , M. Kuhlmann , M. Kümmel , S. Leiber , P. Musiol , A. Mustafa , M. Pelizäus , A. Pitka , G. Reicherz , M. Richter , C. Schnier , T. Schröder , S. Sersin , L. Sohl , C. Sowa , M. Steinke , T. Triffterer , U. Wiedner , R. Beck , C. Hammann , J. Hartmann , B. Ketzer , M. Kube , M. Rossbach , C. Schmidt , R. Schmitz , U. Thoma , M. Urban , A. Bianconi , M. Bragadireanu , D. Pantea , W. Czyzycki , M. Domagala , G. Filo , J. Jaworowski , M. Krawczyk , E. Lisowski , F. Lisowski , M. Michalek , J. Plazek , K. Korcyl , A. Kozela , P. Kulessa , P. Lebiedowicz , K. Pysz , W. Schäfer , A. Szczurek , T. Fiutowski , M. Idzik , B. Mindur , K. Swientek , J. Biernat , B. Kamys , S. Kistryn , G. Korcyl , W. Krzemien , A. Magiera , P. Moskal , W. Przygoda , Z. Rudy , P. Salabura , J. Smyrski , P. Strzempek , A. Wronska , I. Augustin , R. Böhm , I. Lehmann , D. Nicmorus Marinescu , L. Schmitt , V. Varentsov , M. Al-Turany , A. Belias , H. Deppe , N. Divani Veis , R. Dzhygadlo , H. Flemming , A. Gerhardt , K. Götzen , R. Karabowicz , U. Kurilla , D. Lehmann , S. Löchner , J. Lühning , U. Lynen , S. Nakhoul , H. Orth , K. Peters , T. Saito , G. Schepers , C. J. Schmidt , C. Schwarz , J. Schwiening , A. Täschner , M. Traxler , B. Voss , P. Wieczorek , A. Wilms , V. Abazov , G. Alexeev , V. A. Arefiev , V. Astakhov , M. Yu. Barabanov , B. V. Batyunya , V. Kh. Dodokhov , A. Efremov , A. Fechtchenko , A. Galoyan , G. Golovanov , E. K. Koshurnikov , Y. Yu. Lobanov , V. I. Lobanov , V. Malyshev , A. G. Olshevskiy , A. A. Piskun , A. Samartsev , M. G. Sapozhnikov , N. B. Skachkov , A. N. Skachkova , E. A. Strokovsky , V. Tokmenin , V. Uzhinsky , A. Verkheev , A. Vodopianov , N. I. Zhuravlev , A. Zinchenko , D. Branford , D. Glazier , D. Watts , M. Böhm , W. Eyrich , A. Lehmann , D. Miehling , M. Pfaffinger , S. Stelter , F. Uhlig , S. Dobbs , K. Seth , A. Tomaradze , T. Xiao , D. Bettoni , A. Ali , A. Hamdi , M. Krebs , F. Nerling , V. Akishina , S. Gorbunov , I. Kisel , G. Kozlov , M. Pugach , M. Zyzak , N. Bianchi , P. Gianotti , C. Guaraldo , V. Lucherini , G. Bracco , S. Bodenschatz , K. T. Brinkmann , V. Di Pietro , S. Diehl , V. Dormenev , M. Düren , E. Etzelmüller , K. Föhl , M. Galuska , T. Geßler , E. Gutz , C. Hahn , A. Hayrapetyan , M. Kesselkaul , W. Kühn , T. Kuske , J. S. Lange , Y. Liang , V. Metag , M. Moritz , M. Nanova , R. Novotny , T. Quagli , A. Riccardi , J. Rieke , M. Schmidt , R. Schnell , H. Stenzel , M. Strickert , U. Thöring , T. Wasem , B. Wohlfahrt , H. G. Zaunick , E. Tomasi-Gustafsson , D. Ireland , G. Rosner , B. Seitz , P. N. Deepak , A. Kulkarni , A. Apostolou , M. Babai , M. Kavatsyuk , H. Loehner , J. Messchendorp , P. Schakel , M. Tiemens , J. C. van der Weele , S. Vejdani , K. Dutta , K. Kalita , H. Sohlbach , M. Bai , L. Bianchi , M. Büscher , A. Derichs , R. Dosdall , A. Erven , V. Fracassi , A. Gillitzer , F. Goldenbaum , D. Grunwald , L. Jokhovets , G. Kemmerling , H. Kleines , A. Lai , A. Lehrach , M. Mikirtychyants , S. Orfanitski , D. Prasuhn , E. Prencipe , J. Pütz , J. Ritman , E. Rosenthal , S. Schadmand , T. Sefzick , V. Serdyuk , G. Sterzenbach , T. Stockmanns , P. Wintz , P. Wüstner , H. Xu , Y. Zhou , Z. Li , X. Ma , H. Xu , V. Rigato , L. Isaksson , P. Achenbach , A. Aycock , O. Corell , A. Denig , M. Distler , M. Hoek , W. Lauth , Z. Liu , H. Merkel , U. Müller , J. Pochodzalla , S. Sanchez , S. Schlimme , C. Sfienti , M. Thiel , M. Zambrana , H. Ahmadi , S. Ahmed , S. Bleser , L. Capozza , M. Cardinali , A. Dbeyssi , A. Ehret , B. Fröhlich , P. Grasemann , S. Haasler , D. Izard , J. Jorge , D. Khaneft , R. Klasen , R. Kliemt , J. Köhler , H. H. Leithoff , D. Lin , F. Maas , S. Maldaner , M. Michel , M. C. Mora Espi , C. Morales Morales , C. Motzko , O. Noll , S. Pflüger , D. Rodriguez Pineiro , M. Steinen , E. Walaa , S. Wolff , I. Zimmermann , A. Fedorov , M. Korzhik , O. Missevitch , P. Balanutsa , V. Chernetsky , A. Demekhin , A. Dolgolenko , P. Fedorets , A. Gerasimov , V. Goryachev , D. Y. Kirin , V. A. Matveev , A. V. Stavinskiy , A. Balashoff , A. Boukharov , O. Malyshev , I. Marishev , V. Chandratre , V. Datar , V. Jha , H. Kumawat , A. K. Mohanty , A. Parmar , A. K. Rai , B. Roy , G. Sonika , C. Fritzsch , S. Grieser , A. K. Hergemöller , B. Hetz , N. Hüsken , A. Khoukaz , J. P. Wessels , C. Herold , K. Khosonthongkee , C. Kobdaj , A. Limphirat , P. Srisawad , Y. Yan , A. E. Blinov , S. Kononov , E. A. Kravchenko , E. Antokhin , M. Barnyakov , A. Yu. Barnyakov , K. Beloborodov , V. E. Blinov , V. S. Bobrovnikov , I. A. Kuyanov , A. P. Onuchin , S. Pivovarov , E. Pyata , S. Serednyakov , Y. Tikhonov , R. Kunne , D. Marchand , B. Ramstein , J. van de Wiele , Y. Wang , G. Boca , V. Burian , M. Finger , M. Finger , A. Nikolovova , M. Pesek , M. Peskova , M. Pfeffer , I. Prochazka , M. Slunecka , P. Gallus , V. Jary , J. Novy , M. Tomasek , M. Virius , V. Vrba , V. Abramov , N. Belikov , S. Bukreeva , A. Davidenko , A. Derevschikov , Y. Goncharenko , V. Grishin , V. Kachanov , V. Kormilitsin , A. Levin , Y. Melnik , N. Minaev , V. Mochalov , D. Morozov , L. Nogach , S. Poslavskiy , A. Ryazantsev , S. Ryzhikov , P. Semenov , I. Shein , A. Uzunian , A. Vasiliev , A. Yakutin , U. Roy , B. Yabsley , S. Belostotski , G. Gavrilov , A. Izotov , S. Manaenkov , O. Miklukho , D. Veretennikov , A. Zhdanov , T. Bäck , B. Cederwall , K. Makonyi , M. Preston , P. E. Tegner , D. Wölbing , S. Godre , M. P. Bussa , S. Marcello , S. Spataro , F. Iazzi , R. Introzzi , A. Lavagno , D. Calvo , P. De Remigis , A. Filippi , G. Mazza , A. Rivetti , R. Wheadon , A. Martin , H. Calen , W. Ikegami Andersson , T. Johansson , A. Kupsc , P. Marciniewski , M. Papenbrock , J. Pettersson , J. Regina , K. Schönning , M. Wolke , J. Diaz , V. Pothodi Chackara , A. Chlopik , G. Kesik , D. Melnychuk , B. Slowinski , A. Trzcinski , M. Wojciechowski , S. Wronka , B. Zwieglinski , P. Bühler , J. Marton , D. Steinschaden , K. Suzuki , E. Widmann , S. Zimmermann , J. Zmeskal

Modern warehouse-scale datacenters commonly collocate multiple jobs on shared machines to improve resource utilization. However, such collocation often leads to performance interference caused by antagonistic jobs that overconsume shared…

Performance · Computer Science 2025-11-25 Sixiang Zhou , Nan Deng , Krzysiek Rzadca , Xiaojun Lin , Y. Charlie Hu

Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator…

Computer Vision and Pattern Recognition · Computer Science 2017-07-12 Li Du , Yuan Du , Yilei Li , Mau-Chung Frank Chang

Deep learning-based point cloud processing plays an important role in various vision tasks, such as autonomous driving, virtual reality (VR), and augmented reality (AR). The submanifold sparse convolutional network (SSCN) has been widely…

Signal Processing · Electrical Eng. & Systems 2022-10-17 Zilun Wang , Wendong Mao , Peixiang Yang , Zhongfeng Wang , Jun Lin

Information-centric Networking (ICN) is an emerging Internet architecture that offers promising features, such as in-network caching and named data addressing, to support the edge computing paradigm, in particular Internet-of-Things (IoT)…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-12-15 Manisha Luthra , Johannes Pfannmüller , Boris Koldehofe , Jonas Höchst , Artur Sterz , Rhaban Hark , Bernd Freisleben

Latency-sensitive and bandwidth-intensive stream processing applications are dominant traffic generators over the Internet network. A stream consists of a continuous sequence of data elements, which require processing in nearly real-time.…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-05-18 Narges Mehran , Dragi Kimovski , Radu Prodan

This paper presents FADE-10G - an integrated solution for modern multichannel measurement systems. Its main aim is a low latency, reliable transmission of measurement data from FPGA-based front-end electronic boards (FEBs) to a…

Networking and Internet Architecture · Computer Science 2015-07-29 Wojciech M. Zabolotny
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