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Fast convolutional ...
Fast convolutional neural networks on FPGAs with hls4ml
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- Aarrestad, Thea (author)
- Organisation européenne pour la recherche nucléaire (CERN),European Organization for Nuclear Research (CERN)
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- Loncar, Vladimir (author)
- Organisation européenne pour la recherche nucléaire (CERN),European Organization for Nuclear Research (CERN),Univerzitet u Beogradu,University of Belgrade
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- Ghielmetti, Nicolo (author)
- Politecnico di Milano,Polytechnic University of Milan,Organisation européenne pour la recherche nucléaire (CERN),European Organization for Nuclear Research (CERN)
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- Pierini, Maurizio (author)
- Organisation européenne pour la recherche nucléaire (CERN),European Organization for Nuclear Research (CERN)
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- Summers, Sioni (author)
- Organisation européenne pour la recherche nucléaire (CERN),European Organization for Nuclear Research (CERN)
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- Ngadiuba, Jennifer (author)
- California Institute of Technology (Caltech)
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- Petersson, Christoffer, 1979 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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Linander, Hampus (author)
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- Iiyama, Yutaro (author)
- University of Tokyo, Japan
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- Di Guglielmo, Giuseppe (author)
- Columbia University in the City of New York
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- Duarte, Javier (author)
- University of California
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- Harris, Philip (author)
- Massachusetts Institute of Technology (MIT)
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- Rankin, Dylan (author)
- Massachusetts Institute of Technology (MIT)
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Jindariani, Sergo (author)
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Pedro, Kevin (author)
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Nhan Tran, (author)
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- Liu, Mia (author)
- Purdue University
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Kreinar, Edward (author)
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- Wu, Zhenbin (author)
- University of Illinois
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- Hoang, Duc (author)
- Rhodes College
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(creator_code:org_t)
- 2021-07-16
- 2021
- English.
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In: Machine Learning: Science and Technology. - : IOP Publishing. - 2632-2153. ; 2:4
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https://doi.org/10.1...
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Abstract
Subject headings
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- We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on field-programmable gate arrays (FPGAs). By extending the hls4ml library, we demonstrate an inference latency of 5 mu s using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detectors. In particular, we discuss pruning and quantization-aware training, and demonstrate how resource utilization can be significantly reduced with little to no loss in model accuracy. We show that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)
Keyword
- FPGA
- convolutional neural network
- deep learning
Publication and Content Type
- art (subject category)
- ref (subject category)
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To the university's database
- By the author/editor
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Aarrestad, Thea
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Loncar, Vladimir
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Ghielmetti, Nico ...
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Pierini, Maurizi ...
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Summers, Sioni
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Ngadiuba, Jennif ...
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show more...
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Petersson, Chris ...
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Linander, Hampus
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Iiyama, Yutaro
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Di Guglielmo, Gi ...
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Duarte, Javier
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Harris, Philip
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Rankin, Dylan
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Jindariani, Serg ...
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Pedro, Kevin
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Nhan Tran,
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Liu, Mia
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Kreinar, Edward
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Wu, Zhenbin
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Hoang, Duc
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show less...
- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Computer and Inf ...
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and Computer Enginee ...
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Computer and Inf ...
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and Bioinformatics
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- ENGINEERING AND TECHNOLOGY
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ENGINEERING AND ...
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and Electrical Engin ...
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and Embedded Systems
- Articles in the publication
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Machine Learning ...
- By the university
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Chalmers University of Technology