FPGA‐accelerated deep convolutional neural networks for high throughput and energy efficiency Article Swipe
Yuran Qiao
,
Junzhong Shen
,
Tao Xiao
,
Qianming Yang
,
Mei Wen
,
Chunyuan Zhang
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1002/cpe.3850
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1002/cpe.3850
Summary Recent breakthroughs in the deep convolutional neural networks (CNNs) have led to great improvements in the accuracy of both vision and auditory systems. Characterized by their deep structures and large numbers of parameters, deep CNNs challenge the computational performance of today. Hardware specialization in the form of field‐programmable gate array offers a promising path towards major leaps in computational performance while achieving high‐energy efficiency. In this paper, we focus on accelerating deep CNNs using the Xilinx Zynq‐zq7045 FPGA SoC. As most of the computational workload can be converted to matrix multiplications, we adopt a matrix multiplier‐based accelerator architecture. Dedicated units are designed to eliminate the conversion overhead. We also design a customized memory system according to the memory access pattern of CNNs. To make the accelerator easily usable by application developers, our accelerator supports Caffe, which is a widely used software framework of deep CNN. Different CNN models can be adopted by our accelerator, with good performance portability. The experimental results show that for a typical application of CNN, image classification, an average throughout of 77.8 GFLOPS is achieved, while the energy efficiency is 4.7× better than an Nvidia K20 GPGPU. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd
Related Topics
Concepts
Computer science
Convolutional neural network
Field-programmable gate array
FLOPS
Parallel computing
Deep learning
Computer architecture
Efficient energy use
Software portability
CUDA
Hardware acceleration
Artificial intelligence
Overhead (engineering)
Computer engineering
Embedded system
Programming language
Operating system
Engineering
Electrical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/cpe.3850
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.3850
- OA Status
- hybrid
- Cited By
- 51
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2404540148
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2404540148Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/cpe.3850Digital Object Identifier
- Title
-
FPGA‐accelerated deep convolutional neural networks for high throughput and energy efficiencyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-05-06Full publication date if available
- Authors
-
Yuran Qiao, Junzhong Shen, Tao Xiao, Qianming Yang, Mei Wen, Chunyuan ZhangList of authors in order
- Landing page
-
https://doi.org/10.1002/cpe.3850Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.3850Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.3850Direct OA link when available
- Concepts
-
Computer science, Convolutional neural network, Field-programmable gate array, FLOPS, Parallel computing, Deep learning, Computer architecture, Efficient energy use, Software portability, CUDA, Hardware acceleration, Artificial intelligence, Overhead (engineering), Computer engineering, Embedded system, Programming language, Operating system, Engineering, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
51Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 4, 2022: 3, 2021: 7, 2020: 15Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2404540148 |
|---|---|
| doi | https://doi.org/10.1002/cpe.3850 |
| ids.doi | https://doi.org/10.1002/cpe.3850 |
| ids.mag | 2404540148 |
| ids.openalex | https://openalex.org/W2404540148 |
| fwci | 3.51083006 |
| type | article |
| title | FPGA‐accelerated deep convolutional neural networks for high throughput and energy efficiency |
| awards[0].id | https://openalex.org/G7699651490 |
| awards[0].funder_id | https://openalex.org/F4320336024 |
| awards[0].display_name | |
| awards[0].funder_award_id | 20124307130004 |
| awards[0].funder_display_name | Specialized Research Fund for the Doctoral Program of Higher Education of China |
| awards[1].id | https://openalex.org/G5345322127 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | NSFC 61272145 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 20 |
| biblio.volume | 29 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10036 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Advanced Neural Network Applications |
| topics[1].id | https://openalex.org/T11689 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9972000122070312 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Adversarial Robustness in Machine Learning |
| topics[2].id | https://openalex.org/T10627 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9959999918937683 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Advanced Image and Video Retrieval Techniques |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320336024 |
| funders[1].ror | |
| funders[1].display_name | Specialized Research Fund for the Doctoral Program of Higher Education of China |
| is_xpac | False |
| apc_list.value | 4740 |
| apc_list.currency | USD |
| apc_list.value_usd | 4740 |
| apc_paid.value | 4740 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 4740 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8331104516983032 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C81363708 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7067133188247681 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[1].display_name | Convolutional neural network |
| concepts[2].id | https://openalex.org/C42935608 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6873674988746643 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q190411 |
| concepts[2].display_name | Field-programmable gate array |
| concepts[3].id | https://openalex.org/C3826847 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6358623504638672 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q188768 |
| concepts[3].display_name | FLOPS |
| concepts[4].id | https://openalex.org/C173608175 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5143605470657349 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q232661 |
| concepts[4].display_name | Parallel computing |
| concepts[5].id | https://openalex.org/C108583219 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5019657611846924 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[5].display_name | Deep learning |
| concepts[6].id | https://openalex.org/C118524514 |
| concepts[6].level | 1 |
| concepts[6].score | 0.499359130859375 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q173212 |
| concepts[6].display_name | Computer architecture |
| concepts[7].id | https://openalex.org/C2742236 |
| concepts[7].level | 2 |
| concepts[7].score | 0.49000540375709534 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q924713 |
| concepts[7].display_name | Efficient energy use |
| concepts[8].id | https://openalex.org/C63000827 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4867734909057617 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3080428 |
| concepts[8].display_name | Software portability |
| concepts[9].id | https://openalex.org/C2778119891 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4676997661590576 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q477690 |
| concepts[9].display_name | CUDA |
| concepts[10].id | https://openalex.org/C13164978 |
| concepts[10].level | 3 |
| concepts[10].score | 0.45167556405067444 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q600158 |
| concepts[10].display_name | Hardware acceleration |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.42407655715942383 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C2779960059 |
| concepts[12].level | 2 |
| concepts[12].score | 0.41708970069885254 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7113681 |
| concepts[12].display_name | Overhead (engineering) |
| concepts[13].id | https://openalex.org/C113775141 |
| concepts[13].level | 1 |
| concepts[13].score | 0.39621874690055847 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q428691 |
| concepts[13].display_name | Computer engineering |
| concepts[14].id | https://openalex.org/C149635348 |
| concepts[14].level | 1 |
| concepts[14].score | 0.372876912355423 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q193040 |
| concepts[14].display_name | Embedded system |
| concepts[15].id | https://openalex.org/C199360897 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[15].display_name | Programming language |
| concepts[16].id | https://openalex.org/C111919701 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[16].display_name | Operating system |
| concepts[17].id | https://openalex.org/C127413603 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[17].display_name | Engineering |
| concepts[18].id | https://openalex.org/C119599485 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[18].display_name | Electrical engineering |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8331104516983032 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[1].score | 0.7067133188247681 |
| keywords[1].display_name | Convolutional neural network |
| keywords[2].id | https://openalex.org/keywords/field-programmable-gate-array |
| keywords[2].score | 0.6873674988746643 |
| keywords[2].display_name | Field-programmable gate array |
| keywords[3].id | https://openalex.org/keywords/flops |
| keywords[3].score | 0.6358623504638672 |
| keywords[3].display_name | FLOPS |
| keywords[4].id | https://openalex.org/keywords/parallel-computing |
| keywords[4].score | 0.5143605470657349 |
| keywords[4].display_name | Parallel computing |
| keywords[5].id | https://openalex.org/keywords/deep-learning |
| keywords[5].score | 0.5019657611846924 |
| keywords[5].display_name | Deep learning |
| keywords[6].id | https://openalex.org/keywords/computer-architecture |
| keywords[6].score | 0.499359130859375 |
| keywords[6].display_name | Computer architecture |
| keywords[7].id | https://openalex.org/keywords/efficient-energy-use |
| keywords[7].score | 0.49000540375709534 |
| keywords[7].display_name | Efficient energy use |
| keywords[8].id | https://openalex.org/keywords/software-portability |
| keywords[8].score | 0.4867734909057617 |
| keywords[8].display_name | Software portability |
| keywords[9].id | https://openalex.org/keywords/cuda |
| keywords[9].score | 0.4676997661590576 |
| keywords[9].display_name | CUDA |
| keywords[10].id | https://openalex.org/keywords/hardware-acceleration |
| keywords[10].score | 0.45167556405067444 |
| keywords[10].display_name | Hardware acceleration |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.42407655715942383 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/overhead |
| keywords[12].score | 0.41708970069885254 |
| keywords[12].display_name | Overhead (engineering) |
| keywords[13].id | https://openalex.org/keywords/computer-engineering |
| keywords[13].score | 0.39621874690055847 |
| keywords[13].display_name | Computer engineering |
| keywords[14].id | https://openalex.org/keywords/embedded-system |
| keywords[14].score | 0.372876912355423 |
| keywords[14].display_name | Embedded system |
| language | en |
| locations[0].id | doi:10.1002/cpe.3850 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S11065456 |
| locations[0].source.issn | 1532-0626, 1532-0634 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1532-0626 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Concurrency and Computation Practice and Experience |
| locations[0].source.host_organization | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_name | Wiley |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_lineage_names | Wiley |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.3850 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Concurrency and Computation: Practice and Experience |
| locations[0].landing_page_url | https://doi.org/10.1002/cpe.3850 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5102984199 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5488-3545 |
| authorships[0].author.display_name | Yuran Qiao |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I170215575 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[0].institutions[0].id | https://openalex.org/I170215575 |
| authorships[0].institutions[0].ror | https://ror.org/05d2yfz11 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I170215575 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | National University of Defense Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yuran Qiao |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[1].author.id | https://openalex.org/A5037428152 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6233-6800 |
| authorships[1].author.display_name | Junzhong Shen |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I170215575 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[1].institutions[0].id | https://openalex.org/I170215575 |
| authorships[1].institutions[0].ror | https://ror.org/05d2yfz11 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I170215575 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | National University of Defense Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Junzhong Shen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[2].author.id | https://openalex.org/A5100600705 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4070-585X |
| authorships[2].author.display_name | Tao Xiao |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I170215575 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[2].institutions[0].id | https://openalex.org/I170215575 |
| authorships[2].institutions[0].ror | https://ror.org/05d2yfz11 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I170215575 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | National University of Defense Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Tao Xiao |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[3].author.id | https://openalex.org/A5113537087 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Qianming Yang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I170215575 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[3].institutions[0].id | https://openalex.org/I170215575 |
| authorships[3].institutions[0].ror | https://ror.org/05d2yfz11 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I170215575 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | National University of Defense Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Qianming Yang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[4].author.id | https://openalex.org/A5101937502 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5875-3297 |
| authorships[4].author.display_name | Mei Wen |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I170215575 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[4].institutions[0].id | https://openalex.org/I170215575 |
| authorships[4].institutions[0].ror | https://ror.org/05d2yfz11 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I170215575 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | National University of Defense Technology |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Mei Wen |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[5].author.id | https://openalex.org/A5100710936 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-0944-2708 |
| authorships[5].author.display_name | Chunyuan Zhang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I170215575 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| authorships[5].institutions[0].id | https://openalex.org/I170215575 |
| authorships[5].institutions[0].ror | https://ror.org/05d2yfz11 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I170215575 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | National University of Defense Technology |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Chunyuan Zhang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Computer, State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.3850 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | FPGA‐accelerated deep convolutional neural networks for high throughput and energy efficiency |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10036 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Neural Network Applications |
| related_works | https://openalex.org/W3020739840, https://openalex.org/W2913998709, https://openalex.org/W3177128669, https://openalex.org/W4386875822, https://openalex.org/W4385574943, https://openalex.org/W2016659453, https://openalex.org/W4292794827, https://openalex.org/W4224939635, https://openalex.org/W4319952061, https://openalex.org/W4280636456 |
| cited_by_count | 51 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 3 |
| counts_by_year[3].year | 2021 |
| counts_by_year[3].cited_by_count | 7 |
| counts_by_year[4].year | 2020 |
| counts_by_year[4].cited_by_count | 15 |
| counts_by_year[5].year | 2019 |
| counts_by_year[5].cited_by_count | 10 |
| counts_by_year[6].year | 2018 |
| counts_by_year[6].cited_by_count | 9 |
| counts_by_year[7].year | 2017 |
| counts_by_year[7].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1002/cpe.3850 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S11065456 |
| best_oa_location.source.issn | 1532-0626, 1532-0634 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1532-0626 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Concurrency and Computation Practice and Experience |
| best_oa_location.source.host_organization | https://openalex.org/P4310320595 |
| best_oa_location.source.host_organization_name | Wiley |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| best_oa_location.source.host_organization_lineage_names | Wiley |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.3850 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Concurrency and Computation: Practice and Experience |
| best_oa_location.landing_page_url | https://doi.org/10.1002/cpe.3850 |
| primary_location.id | doi:10.1002/cpe.3850 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S11065456 |
| primary_location.source.issn | 1532-0626, 1532-0634 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1532-0626 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Concurrency and Computation Practice and Experience |
| primary_location.source.host_organization | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_name | Wiley |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_lineage_names | Wiley |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.3850 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Concurrency and Computation: Practice and Experience |
| primary_location.landing_page_url | https://doi.org/10.1002/cpe.3850 |
| publication_date | 2016-05-06 |
| publication_year | 2016 |
| referenced_works | https://openalex.org/W2160815625, https://openalex.org/W2102605133, https://openalex.org/W2044535169, https://openalex.org/W1945588072, https://openalex.org/W1949232123, https://openalex.org/W1990315422, https://openalex.org/W2094756095, https://openalex.org/W2053968820, https://openalex.org/W6638783484, https://openalex.org/W2155893237, https://openalex.org/W2061624656, https://openalex.org/W2255691511, https://openalex.org/W2125203716, https://openalex.org/W6630649318, https://openalex.org/W3004171485, https://openalex.org/W2117696986, https://openalex.org/W2152839228, https://openalex.org/W6637151318, https://openalex.org/W7007740490, https://openalex.org/W1845051632, https://openalex.org/W2082238959, https://openalex.org/W2025890876, https://openalex.org/W753012316, https://openalex.org/W567531775, https://openalex.org/W4302296459, https://openalex.org/W1509966554, https://openalex.org/W2963674932, https://openalex.org/W2188696834, https://openalex.org/W2101281312, https://openalex.org/W2163605009, https://openalex.org/W4297790889, https://openalex.org/W2009832130, https://openalex.org/W1841592590, https://openalex.org/W1667652561, https://openalex.org/W2272300165 |
| referenced_works_count | 35 |
| abstract_inverted_index.a | 52, 94, 111, 138, 165 |
| abstract_inverted_index.As | 80 |
| abstract_inverted_index.In | 65 |
| abstract_inverted_index.To | 123 |
| abstract_inverted_index.We | 108 |
| abstract_inverted_index.an | 172, 188 |
| abstract_inverted_index.be | 87, 150 |
| abstract_inverted_index.by | 25, 129, 152, 203 |
| abstract_inverted_index.in | 3, 15, 44, 58 |
| abstract_inverted_index.is | 137, 178, 184 |
| abstract_inverted_index.of | 18, 32, 40, 47, 82, 121, 143, 168, 175 |
| abstract_inverted_index.on | 70 |
| abstract_inverted_index.to | 12, 89, 103, 116 |
| abstract_inverted_index.we | 68, 92 |
| abstract_inverted_index.© | 192 |
| abstract_inverted_index.CNN | 147 |
| abstract_inverted_index.K20 | 190 |
| abstract_inverted_index.Ltd | 208 |
| abstract_inverted_index.The | 159, 194 |
| abstract_inverted_index.and | 21, 29, 197, 200 |
| abstract_inverted_index.are | 101 |
| abstract_inverted_index.can | 86, 149 |
| abstract_inverted_index.for | 164 |
| abstract_inverted_index.led | 11 |
| abstract_inverted_index.our | 132, 153 |
| abstract_inverted_index.the | 4, 16, 37, 45, 75, 83, 105, 117, 125, 181 |
| abstract_inverted_index.2016 | 193 |
| abstract_inverted_index.77.8 | 176 |
| abstract_inverted_index.CNN, | 169 |
| abstract_inverted_index.CNN. | 145 |
| abstract_inverted_index.CNNs | 35, 73 |
| abstract_inverted_index.FPGA | 78 |
| abstract_inverted_index.John | 204 |
| abstract_inverted_index.SoC. | 79 |
| abstract_inverted_index.Sons | 207 |
| abstract_inverted_index.also | 109 |
| abstract_inverted_index.both | 19 |
| abstract_inverted_index.deep | 5, 27, 34, 72, 144 |
| abstract_inverted_index.form | 46 |
| abstract_inverted_index.gate | 49 |
| abstract_inverted_index.good | 156 |
| abstract_inverted_index.have | 10 |
| abstract_inverted_index.make | 124 |
| abstract_inverted_index.most | 81 |
| abstract_inverted_index.path | 54 |
| abstract_inverted_index.show | 162 |
| abstract_inverted_index.than | 187 |
| abstract_inverted_index.that | 163 |
| abstract_inverted_index.this | 66 |
| abstract_inverted_index.used | 140 |
| abstract_inverted_index.with | 155 |
| abstract_inverted_index.& | 206 |
| abstract_inverted_index.4.7× | 185 |
| abstract_inverted_index.CNNs. | 122 |
| abstract_inverted_index.Wiley | 205 |
| abstract_inverted_index.adopt | 93 |
| abstract_inverted_index.array | 50 |
| abstract_inverted_index.focus | 69 |
| abstract_inverted_index.great | 13 |
| abstract_inverted_index.image | 170 |
| abstract_inverted_index.large | 30 |
| abstract_inverted_index.leaps | 57 |
| abstract_inverted_index.major | 56 |
| abstract_inverted_index.their | 26 |
| abstract_inverted_index.units | 100 |
| abstract_inverted_index.using | 74 |
| abstract_inverted_index.which | 136 |
| abstract_inverted_index.while | 61, 180 |
| abstract_inverted_index.(CNNs) | 9 |
| abstract_inverted_index.Caffe, | 135 |
| abstract_inverted_index.GFLOPS | 177 |
| abstract_inverted_index.GPGPU. | 191 |
| abstract_inverted_index.Nvidia | 189 |
| abstract_inverted_index.Recent | 1 |
| abstract_inverted_index.Xilinx | 76 |
| abstract_inverted_index.access | 119 |
| abstract_inverted_index.better | 186 |
| abstract_inverted_index.design | 110 |
| abstract_inverted_index.easily | 127 |
| abstract_inverted_index.energy | 182 |
| abstract_inverted_index.matrix | 90, 95 |
| abstract_inverted_index.memory | 113, 118 |
| abstract_inverted_index.models | 148 |
| abstract_inverted_index.neural | 7 |
| abstract_inverted_index.offers | 51 |
| abstract_inverted_index.paper, | 67 |
| abstract_inverted_index.system | 114 |
| abstract_inverted_index.today. | 41 |
| abstract_inverted_index.usable | 128 |
| abstract_inverted_index.vision | 20 |
| abstract_inverted_index.widely | 139 |
| abstract_inverted_index.Summary | 0 |
| abstract_inverted_index.adopted | 151 |
| abstract_inverted_index.average | 173 |
| abstract_inverted_index.numbers | 31 |
| abstract_inverted_index.pattern | 120 |
| abstract_inverted_index.results | 161 |
| abstract_inverted_index.towards | 55 |
| abstract_inverted_index.typical | 166 |
| abstract_inverted_index.Authors. | 195 |
| abstract_inverted_index.Hardware | 42 |
| abstract_inverted_index.Practice | 199 |
| abstract_inverted_index.accuracy | 17 |
| abstract_inverted_index.auditory | 22 |
| abstract_inverted_index.designed | 102 |
| abstract_inverted_index.networks | 8 |
| abstract_inverted_index.software | 141 |
| abstract_inverted_index.supports | 134 |
| abstract_inverted_index.systems. | 23 |
| abstract_inverted_index.workload | 85 |
| abstract_inverted_index.Dedicated | 99 |
| abstract_inverted_index.Different | 146 |
| abstract_inverted_index.Published | 202 |
| abstract_inverted_index.according | 115 |
| abstract_inverted_index.achieved, | 179 |
| abstract_inverted_index.achieving | 62 |
| abstract_inverted_index.challenge | 36 |
| abstract_inverted_index.converted | 88 |
| abstract_inverted_index.eliminate | 104 |
| abstract_inverted_index.framework | 142 |
| abstract_inverted_index.overhead. | 107 |
| abstract_inverted_index.promising | 53 |
| abstract_inverted_index.Experience | 201 |
| abstract_inverted_index.conversion | 106 |
| abstract_inverted_index.customized | 112 |
| abstract_inverted_index.efficiency | 183 |
| abstract_inverted_index.structures | 28 |
| abstract_inverted_index.throughout | 174 |
| abstract_inverted_index.Concurrency | 196 |
| abstract_inverted_index.accelerator | 97, 126, 133 |
| abstract_inverted_index.application | 130, 167 |
| abstract_inverted_index.developers, | 131 |
| abstract_inverted_index.efficiency. | 64 |
| abstract_inverted_index.parameters, | 33 |
| abstract_inverted_index.performance | 39, 60, 157 |
| abstract_inverted_index.Computation: | 198 |
| abstract_inverted_index.accelerating | 71 |
| abstract_inverted_index.accelerator, | 154 |
| abstract_inverted_index.experimental | 160 |
| abstract_inverted_index.improvements | 14 |
| abstract_inverted_index.portability. | 158 |
| abstract_inverted_index.Characterized | 24 |
| abstract_inverted_index.Zynq‐zq7045 | 77 |
| abstract_inverted_index.architecture. | 98 |
| abstract_inverted_index.breakthroughs | 2 |
| abstract_inverted_index.computational | 38, 59, 84 |
| abstract_inverted_index.convolutional | 6 |
| abstract_inverted_index.high‐energy | 63 |
| abstract_inverted_index.specialization | 43 |
| abstract_inverted_index.classification, | 171 |
| abstract_inverted_index.multiplications, | 91 |
| abstract_inverted_index.multiplier‐based | 96 |
| abstract_inverted_index.field‐programmable | 48 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5102984199 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I170215575 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.95632919 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |