Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs Article Swipe
Partha Maji
,
Andrew Mundy
,
Ganesh Dasika
,
Jesse Beu
,
Matthew Mattina
,
Robert Mullins
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/emc249363.2019.00008
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/emc249363.2019.00008
The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs). Although there has been a lot of research done on model and algorithmic optimization of CNN, little attention has been paid to the efficient implementation of these algorithms on embedded CPUs, which usually have very limited memory and low power budget. This paper aims to fill this gap and focuses on the efficient implementation of Winograd or Cook-Toom based convolution on modern Arm Cortex-A CPUs, widely used in mobile devices today. Specifically, we demonstrate a reduction in inference latency by using a set of optimization strategies that improve the utilization of computational resources, and by effectively leveraging the ARMv8-A NEON SIMD instruction set. We evaluated our proposed region-wise multi-channel implementations on Arm Cortex-A73 platform using several representative CNNs. The results show significant performance improvements in full network, up to 60%, over existing im2row/im2col based optimization techniques
Related Topics
Concepts
Computer science
FLOPS
SIMD
Convolutional neural network
Parallel computing
Latency (audio)
Kernel (algebra)
ARM architecture
Convolution (computer science)
Inference
Reduction (mathematics)
Set (abstract data type)
Computer engineering
Embedded system
Artificial intelligence
Artificial neural network
Telecommunications
Programming language
Mathematics
Geometry
Combinatorics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/emc249363.2019.00008
- OA Status
- green
- Cited By
- 1
- References
- 7
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2918829529
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2918829529Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/emc249363.2019.00008Digital Object Identifier
- Title
-
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-02-01Full publication date if available
- Authors
-
Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse Beu, Matthew Mattina, Robert MullinsList of authors in order
- Landing page
-
https://doi.org/10.1109/emc249363.2019.00008Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1903.01521Direct OA link when available
- Concepts
-
Computer science, FLOPS, SIMD, Convolutional neural network, Parallel computing, Latency (audio), Kernel (algebra), ARM architecture, Convolution (computer science), Inference, Reduction (mathematics), Set (abstract data type), Computer engineering, Embedded system, Artificial intelligence, Artificial neural network, Telecommunications, Programming language, Mathematics, Geometry, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2020: 1Per-year citation counts (last 5 years)
- References (count)
-
7Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2918829529 |
|---|---|
| doi | https://doi.org/10.1109/emc249363.2019.00008 |
| ids.doi | https://doi.org/10.48550/arxiv.1903.01521 |
| ids.mag | 2918829529 |
| ids.openalex | https://openalex.org/W2918829529 |
| fwci | 0.10688576 |
| type | preprint |
| title | Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 5 |
| biblio.first_page | 1 |
| 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/T10502 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9994999766349792 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Advanced Memory and Neural Computing |
| topics[2].id | https://openalex.org/T11992 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9991000294685364 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | CCD and CMOS Imaging Sensors |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8182154893875122 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C3826847 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6444084644317627 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q188768 |
| concepts[1].display_name | FLOPS |
| concepts[2].id | https://openalex.org/C150552126 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5786474347114563 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q339387 |
| concepts[2].display_name | SIMD |
| concepts[3].id | https://openalex.org/C81363708 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5240076780319214 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[3].display_name | Convolutional neural network |
| concepts[4].id | https://openalex.org/C173608175 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5111992955207825 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q232661 |
| concepts[4].display_name | Parallel computing |
| concepts[5].id | https://openalex.org/C82876162 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5010101795196533 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17096504 |
| concepts[5].display_name | Latency (audio) |
| concepts[6].id | https://openalex.org/C74193536 |
| concepts[6].level | 2 |
| concepts[6].score | 0.48095956444740295 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q574844 |
| concepts[6].display_name | Kernel (algebra) |
| concepts[7].id | https://openalex.org/C26771161 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46575576066970825 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q16980 |
| concepts[7].display_name | ARM architecture |
| concepts[8].id | https://openalex.org/C45347329 |
| concepts[8].level | 3 |
| concepts[8].score | 0.45168548822402954 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q5166604 |
| concepts[8].display_name | Convolution (computer science) |
| concepts[9].id | https://openalex.org/C2776214188 |
| concepts[9].level | 2 |
| concepts[9].score | 0.44247886538505554 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[9].display_name | Inference |
| concepts[10].id | https://openalex.org/C111335779 |
| concepts[10].level | 2 |
| concepts[10].score | 0.43697887659072876 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q3454686 |
| concepts[10].display_name | Reduction (mathematics) |
| concepts[11].id | https://openalex.org/C177264268 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4167405366897583 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[11].display_name | Set (abstract data type) |
| concepts[12].id | https://openalex.org/C113775141 |
| concepts[12].level | 1 |
| concepts[12].score | 0.4156317710876465 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q428691 |
| concepts[12].display_name | Computer engineering |
| concepts[13].id | https://openalex.org/C149635348 |
| concepts[13].level | 1 |
| concepts[13].score | 0.284771203994751 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q193040 |
| concepts[13].display_name | Embedded system |
| concepts[14].id | https://openalex.org/C154945302 |
| concepts[14].level | 1 |
| concepts[14].score | 0.2221546769142151 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[14].display_name | Artificial intelligence |
| concepts[15].id | https://openalex.org/C50644808 |
| concepts[15].level | 2 |
| concepts[15].score | 0.21064496040344238 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[15].display_name | Artificial neural network |
| concepts[16].id | https://openalex.org/C76155785 |
| concepts[16].level | 1 |
| concepts[16].score | 0.11097687482833862 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[16].display_name | Telecommunications |
| concepts[17].id | https://openalex.org/C199360897 |
| concepts[17].level | 1 |
| concepts[17].score | 0.08470609784126282 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[17].display_name | Programming language |
| concepts[18].id | https://openalex.org/C33923547 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0842239260673523 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[18].display_name | Mathematics |
| concepts[19].id | https://openalex.org/C2524010 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[19].display_name | Geometry |
| concepts[20].id | https://openalex.org/C114614502 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q76592 |
| concepts[20].display_name | Combinatorics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8182154893875122 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/flops |
| keywords[1].score | 0.6444084644317627 |
| keywords[1].display_name | FLOPS |
| keywords[2].id | https://openalex.org/keywords/simd |
| keywords[2].score | 0.5786474347114563 |
| keywords[2].display_name | SIMD |
| keywords[3].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[3].score | 0.5240076780319214 |
| keywords[3].display_name | Convolutional neural network |
| keywords[4].id | https://openalex.org/keywords/parallel-computing |
| keywords[4].score | 0.5111992955207825 |
| keywords[4].display_name | Parallel computing |
| keywords[5].id | https://openalex.org/keywords/latency |
| keywords[5].score | 0.5010101795196533 |
| keywords[5].display_name | Latency (audio) |
| keywords[6].id | https://openalex.org/keywords/kernel |
| keywords[6].score | 0.48095956444740295 |
| keywords[6].display_name | Kernel (algebra) |
| keywords[7].id | https://openalex.org/keywords/arm-architecture |
| keywords[7].score | 0.46575576066970825 |
| keywords[7].display_name | ARM architecture |
| keywords[8].id | https://openalex.org/keywords/convolution |
| keywords[8].score | 0.45168548822402954 |
| keywords[8].display_name | Convolution (computer science) |
| keywords[9].id | https://openalex.org/keywords/inference |
| keywords[9].score | 0.44247886538505554 |
| keywords[9].display_name | Inference |
| keywords[10].id | https://openalex.org/keywords/reduction |
| keywords[10].score | 0.43697887659072876 |
| keywords[10].display_name | Reduction (mathematics) |
| keywords[11].id | https://openalex.org/keywords/set |
| keywords[11].score | 0.4167405366897583 |
| keywords[11].display_name | Set (abstract data type) |
| keywords[12].id | https://openalex.org/keywords/computer-engineering |
| keywords[12].score | 0.4156317710876465 |
| keywords[12].display_name | Computer engineering |
| keywords[13].id | https://openalex.org/keywords/embedded-system |
| keywords[13].score | 0.284771203994751 |
| keywords[13].display_name | Embedded system |
| keywords[14].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[14].score | 0.2221546769142151 |
| keywords[14].display_name | Artificial intelligence |
| keywords[15].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[15].score | 0.21064496040344238 |
| keywords[15].display_name | Artificial neural network |
| keywords[16].id | https://openalex.org/keywords/telecommunications |
| keywords[16].score | 0.11097687482833862 |
| keywords[16].display_name | Telecommunications |
| keywords[17].id | https://openalex.org/keywords/programming-language |
| keywords[17].score | 0.08470609784126282 |
| keywords[17].display_name | Programming language |
| keywords[18].id | https://openalex.org/keywords/mathematics |
| keywords[18].score | 0.0842239260673523 |
| keywords[18].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1109/emc249363.2019.00008 |
| locations[0].is_oa | False |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | 2019 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) |
| locations[0].landing_page_url | https://doi.org/10.1109/emc249363.2019.00008 |
| locations[1].id | pmh:oai:arXiv.org:1903.01521 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | https://arxiv.org/pdf/1903.01521 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://arxiv.org/abs/1903.01521 |
| locations[2].id | doi:10.48550/arxiv.1903.01521 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.48550/arxiv.1903.01521 |
| locations[3].id | mag:2918829529 |
| locations[3].is_oa | False |
| locations[3].source | |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | |
| locations[3].raw_type | |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | |
| indexed_in | arxiv, crossref, datacite |
| authorships[0].author.id | https://openalex.org/A5016261879 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1919-1228 |
| authorships[0].author.display_name | Partha Maji |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I241749 |
| authorships[0].affiliations[0].raw_affiliation_string | Univ. of Cambridge |
| authorships[0].institutions[0].id | https://openalex.org/I241749 |
| authorships[0].institutions[0].ror | https://ror.org/013meh722 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I241749 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | University of Cambridge |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Partha Maji |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Univ. of Cambridge |
| authorships[1].author.id | https://openalex.org/A5082631490 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0467-653X |
| authorships[1].author.display_name | Andrew Mundy |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I2801109035 |
| authorships[1].affiliations[0].raw_affiliation_string | Arm Research, UK |
| authorships[1].institutions[0].id | https://openalex.org/I2801109035 |
| authorships[1].institutions[0].ror | https://ror.org/04mmhzs81 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I2801109035 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | ARM (United Kingdom) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Andrew Mundy |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Arm Research, UK |
| authorships[2].author.id | https://openalex.org/A5004292742 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Ganesh Dasika |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210156213 |
| authorships[2].affiliations[0].raw_affiliation_string | Arm Research, USA |
| authorships[2].institutions[0].id | https://openalex.org/I4210156213 |
| authorships[2].institutions[0].ror | https://ror.org/05vfrxy92 |
| authorships[2].institutions[0].type | nonprofit |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210156213 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | American Rock Mechanics Association |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ganesh Dasika |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Arm Research, USA |
| authorships[3].author.id | https://openalex.org/A5013531730 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Jesse Beu |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210156213 |
| authorships[3].affiliations[0].raw_affiliation_string | Arm Research, USA |
| authorships[3].institutions[0].id | https://openalex.org/I4210156213 |
| authorships[3].institutions[0].ror | https://ror.org/05vfrxy92 |
| authorships[3].institutions[0].type | nonprofit |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210156213 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | American Rock Mechanics Association |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jesse Beu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Arm Research, USA |
| authorships[4].author.id | https://openalex.org/A5090422974 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Matthew Mattina |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210156213 |
| authorships[4].affiliations[0].raw_affiliation_string | Arm Research, USA |
| authorships[4].institutions[0].id | https://openalex.org/I4210156213 |
| authorships[4].institutions[0].ror | https://ror.org/05vfrxy92 |
| authorships[4].institutions[0].type | nonprofit |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210156213 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | American Rock Mechanics Association |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Matthew Mattina |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Arm Research, USA |
| authorships[5].author.id | https://openalex.org/A5040762162 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-8393-2748 |
| authorships[5].author.display_name | Robert Mullins |
| authorships[5].countries | GB |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I241749 |
| authorships[5].affiliations[0].raw_affiliation_string | Univ. of Cambridge |
| authorships[5].institutions[0].id | https://openalex.org/I241749 |
| authorships[5].institutions[0].ror | https://ror.org/013meh722 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I241749 |
| authorships[5].institutions[0].country_code | GB |
| authorships[5].institutions[0].display_name | University of Cambridge |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Robert Mullins |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Univ. of Cambridge |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/1903.01521 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs |
| has_fulltext | False |
| 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/W3011121930, https://openalex.org/W3204278478, https://openalex.org/W2950649068, https://openalex.org/W3008769740, https://openalex.org/W2890071283, https://openalex.org/W3196029931, https://openalex.org/W2971383048, https://openalex.org/W2949271149, https://openalex.org/W3013525232, https://openalex.org/W2993167790, https://openalex.org/W3151612890, https://openalex.org/W2952713184, https://openalex.org/W2952040342, https://openalex.org/W2591237478, https://openalex.org/W1884620995, https://openalex.org/W2917642026, https://openalex.org/W2980539016, https://openalex.org/W3115410382, https://openalex.org/W3201907778, https://openalex.org/W2483136543 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2020 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | pmh:oai:arXiv.org:1903.01521 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/1903.01521 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/1903.01521 |
| primary_location.id | doi:10.1109/emc249363.2019.00008 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | 2019 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) |
| primary_location.landing_page_url | https://doi.org/10.1109/emc249363.2019.00008 |
| publication_date | 2019-02-01 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W6929373515, https://openalex.org/W2799985340, https://openalex.org/W6685214006, https://openalex.org/W2604319603, https://openalex.org/W2678047256, https://openalex.org/W2172654076, https://openalex.org/W1863850585 |
| referenced_works_count | 7 |
| abstract_inverted_index.a | 26, 96, 103 |
| abstract_inverted_index.We | 125 |
| abstract_inverted_index.by | 101, 116 |
| abstract_inverted_index.in | 89, 98, 146 |
| abstract_inverted_index.of | 5, 14, 28, 36, 47, 76, 105, 112 |
| abstract_inverted_index.on | 31, 50, 72, 82, 132 |
| abstract_inverted_index.or | 2, 78 |
| abstract_inverted_index.to | 8, 43, 66, 150 |
| abstract_inverted_index.up | 149 |
| abstract_inverted_index.we | 94 |
| abstract_inverted_index.Arm | 84, 133 |
| abstract_inverted_index.The | 0, 140 |
| abstract_inverted_index.and | 33, 59, 70, 115 |
| abstract_inverted_index.gap | 69 |
| abstract_inverted_index.has | 24, 40 |
| abstract_inverted_index.lot | 27 |
| abstract_inverted_index.low | 60 |
| abstract_inverted_index.our | 127 |
| abstract_inverted_index.set | 104 |
| abstract_inverted_index.the | 10, 44, 73, 110, 119 |
| abstract_inverted_index.60%, | 151 |
| abstract_inverted_index.CNN, | 37 |
| abstract_inverted_index.NEON | 121 |
| abstract_inverted_index.SIMD | 122 |
| abstract_inverted_index.This | 63 |
| abstract_inverted_index.aims | 65 |
| abstract_inverted_index.been | 25, 41 |
| abstract_inverted_index.deep | 17 |
| abstract_inverted_index.done | 30 |
| abstract_inverted_index.fill | 67 |
| abstract_inverted_index.full | 147 |
| abstract_inverted_index.have | 55 |
| abstract_inverted_index.help | 7 |
| abstract_inverted_index.many | 15 |
| abstract_inverted_index.over | 152 |
| abstract_inverted_index.paid | 42 |
| abstract_inverted_index.set. | 124 |
| abstract_inverted_index.show | 142 |
| abstract_inverted_index.that | 108 |
| abstract_inverted_index.this | 68 |
| abstract_inverted_index.used | 88 |
| abstract_inverted_index.very | 56 |
| abstract_inverted_index.CNNs. | 139 |
| abstract_inverted_index.CPUs, | 52, 86 |
| abstract_inverted_index.based | 80, 155 |
| abstract_inverted_index.class | 4 |
| abstract_inverted_index.model | 32 |
| abstract_inverted_index.paper | 64 |
| abstract_inverted_index.power | 61 |
| abstract_inverted_index.there | 23 |
| abstract_inverted_index.these | 48 |
| abstract_inverted_index.using | 102, 136 |
| abstract_inverted_index.which | 53 |
| abstract_inverted_index.little | 38 |
| abstract_inverted_index.memory | 58 |
| abstract_inverted_index.mobile | 90 |
| abstract_inverted_index.modern | 16, 83 |
| abstract_inverted_index.neural | 19 |
| abstract_inverted_index.reduce | 9 |
| abstract_inverted_index.today. | 92 |
| abstract_inverted_index.widely | 87 |
| abstract_inverted_index.(CNNs). | 21 |
| abstract_inverted_index.ARMv8-A | 120 |
| abstract_inverted_index.budget. | 62 |
| abstract_inverted_index.compute | 12 |
| abstract_inverted_index.devices | 91 |
| abstract_inverted_index.focuses | 71 |
| abstract_inverted_index.improve | 109 |
| abstract_inverted_index.latency | 100 |
| abstract_inverted_index.limited | 57 |
| abstract_inverted_index.overall | 11 |
| abstract_inverted_index.results | 141 |
| abstract_inverted_index.several | 137 |
| abstract_inverted_index.usually | 54 |
| abstract_inverted_index.Although | 22 |
| abstract_inverted_index.Cortex-A | 85 |
| abstract_inverted_index.Winograd | 1, 77 |
| abstract_inverted_index.embedded | 51 |
| abstract_inverted_index.existing | 153 |
| abstract_inverted_index.network, | 148 |
| abstract_inverted_index.networks | 20 |
| abstract_inverted_index.platform | 135 |
| abstract_inverted_index.proposed | 128 |
| abstract_inverted_index.research | 29 |
| abstract_inverted_index.Cook-Toom | 3, 79 |
| abstract_inverted_index.attention | 39 |
| abstract_inverted_index.efficient | 45, 74 |
| abstract_inverted_index.evaluated | 126 |
| abstract_inverted_index.inference | 99 |
| abstract_inverted_index.reduction | 97 |
| abstract_inverted_index.Cortex-A73 | 134 |
| abstract_inverted_index.algorithms | 6, 49 |
| abstract_inverted_index.complexity | 13 |
| abstract_inverted_index.leveraging | 118 |
| abstract_inverted_index.resources, | 114 |
| abstract_inverted_index.strategies | 107 |
| abstract_inverted_index.techniques | 157 |
| abstract_inverted_index.algorithmic | 34 |
| abstract_inverted_index.convolution | 81 |
| abstract_inverted_index.demonstrate | 95 |
| abstract_inverted_index.effectively | 117 |
| abstract_inverted_index.instruction | 123 |
| abstract_inverted_index.performance | 144 |
| abstract_inverted_index.region-wise | 129 |
| abstract_inverted_index.significant | 143 |
| abstract_inverted_index.utilization | 111 |
| abstract_inverted_index.improvements | 145 |
| abstract_inverted_index.optimization | 35, 106, 156 |
| abstract_inverted_index.Specifically, | 93 |
| abstract_inverted_index.computational | 113 |
| abstract_inverted_index.convolutional | 18 |
| abstract_inverted_index.im2row/im2col | 154 |
| abstract_inverted_index.multi-channel | 130 |
| abstract_inverted_index.implementation | 46, 75 |
| abstract_inverted_index.representative | 138 |
| abstract_inverted_index.implementations | 131 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.41169949 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |