A Stage-Wise Soft-Error Detection Scheme for Flip-Flop Based Pipelines in Secure Cloud Servers View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-10

AUTHORS

Hong Zhang, Ying Li, Hongfeng Sun, Qian Yu, Yanchun Yang

ABSTRACT

The shrinking feature sizes make transistors increasingly susceptible to soft errors, which can severely degrade the systems’ RAS (Reliability, Availability, and Serviceability). The tough challenge results from not only increasing SER (soft error rate) of storage cells, but also the increasing susceptibility of combinational logics to soft errors. How to efficiently detect soft errors becomes the primary problem in the Backward Error Recovery (BER) schemes that are cost-effective in soft error tolerance. This paper presents a soft error detection scheme, AUDITOR, for flip-flop based pipelines. The AUDITOR copes with both types of soft errors—single event upset (SEU) and single event transient (SET). We propose a “local-audit” fault detection mechanism, by which each pipeline stage is verified independently and the verifying result registers with a dedicated “audit” bit (V-bit). All the V-bits are distributed across the whole pipeline and synergically monitor the pipeline execution. To relax the constraint of SET detection capability imposed by the inherent fully synchronous operation mode in flip-flop based pipelines, we firstly propose using path-compensation technique to address this constraint. Furthermore, a reuse-based design paradigm is employed to reduce the implementation complexity and area overhead. The AUDITOR possesses robust detection capability and short detection latency, at the expense of about 29 % and 50 % increase in area and power consumption, respectively. More... »

PAGES

61-72

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11265-016-1210-x

DOI

http://dx.doi.org/10.1007/s11265-016-1210-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016247368


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0803", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Computer Software", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Shandong Women\u2019s University", 
          "id": "https://www.grid.ac/institutes/grid.495262.e", 
          "name": [
            "School of Information Technology, Shandong Women\u2019s University, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Hong", 
        "id": "sg:person.07661277730.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07661277730.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Women\u2019s University", 
          "id": "https://www.grid.ac/institutes/grid.495262.e", 
          "name": [
            "School of Information Technology, Shandong Women\u2019s University, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Ying", 
        "id": "sg:person.010456660330.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010456660330.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Women\u2019s University", 
          "id": "https://www.grid.ac/institutes/grid.495262.e", 
          "name": [
            "School of Information Technology, Shandong Women\u2019s University, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sun", 
        "givenName": "Hongfeng", 
        "id": "sg:person.011254240730.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011254240730.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Women\u2019s University", 
          "id": "https://www.grid.ac/institutes/grid.495262.e", 
          "name": [
            "School of Information Technology, Shandong Women\u2019s University, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yu", 
        "givenName": "Qian", 
        "id": "sg:person.010402430663.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010402430663.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Women\u2019s University", 
          "id": "https://www.grid.ac/institutes/grid.495262.e", 
          "name": [
            "School of Information Technology, Shandong Women\u2019s University, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Yanchun", 
        "id": "sg:person.013413650033.89", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013413650033.89"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.future.2015.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009056237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.future.2015.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009056237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.future.2015.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009056237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.future.2015.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009056237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sysarc.2012.07.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040256757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/12.543705", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061088456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jssc.2006.870912", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061329393"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mc.2005.70", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061387546"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mcc.2014.71", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061389921"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mm.2005.104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061408352"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tc.2004.46", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061534014"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tc.2015.2409857", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061535944"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tdmr.2005.855790", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061584089"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tns.2003.813129", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061731883"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tns.2004.840020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061732801"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvlsi.2013.2257902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061816996"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-10", 
    "datePublishedReg": "2017-10-01", 
    "description": "The shrinking feature sizes make transistors increasingly susceptible to soft errors, which can severely degrade the systems\u2019 RAS (Reliability, Availability, and Serviceability). The tough challenge results from not only increasing SER (soft error rate) of storage cells, but also the increasing susceptibility of combinational logics to soft errors. How to efficiently detect soft errors becomes the primary problem in the Backward Error Recovery (BER) schemes that are cost-effective in soft error tolerance. This paper presents a soft error detection scheme, AUDITOR, for flip-flop based pipelines. The AUDITOR copes with both types of soft errors\u2014single event upset (SEU) and single event transient (SET). We propose a \u201clocal-audit\u201d fault detection mechanism, by which each pipeline stage is verified independently and the verifying result registers with a dedicated \u201caudit\u201d bit (V-bit). All the V-bits are distributed across the whole pipeline and synergically monitor the pipeline execution. To relax the constraint of SET detection capability imposed by the inherent fully synchronous operation mode in flip-flop based pipelines, we firstly propose using path-compensation technique to address this constraint. Furthermore, a reuse-based design paradigm is employed to reduce the implementation complexity and area overhead. The AUDITOR possesses robust detection capability and short detection latency, at the expense of about 29 % and 50 % increase in area and power consumption, respectively.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11265-016-1210-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297359", 
        "issn": [
          "0922-5773", 
          "1939-8115"
        ], 
        "name": "Journal of Signal Processing Systems", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "89"
      }
    ], 
    "name": "A Stage-Wise Soft-Error Detection Scheme for Flip-Flop Based Pipelines in Secure Cloud Servers", 
    "pagination": "61-72", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4d6a48f942163773face8010d527058b9c695b1318c039047780d35c26db0491"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11265-016-1210-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1016247368"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11265-016-1210-x", 
      "https://app.dimensions.ai/details/publication/pub.1016247368"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:57", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000347_0000000347/records_89807_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11265-016-1210-x"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11265-016-1210-x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11265-016-1210-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11265-016-1210-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11265-016-1210-x'


 

This table displays all metadata directly associated to this object as RDF triples.

128 TRIPLES      21 PREDICATES      40 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11265-016-1210-x schema:about anzsrc-for:08
2 anzsrc-for:0803
3 schema:author Naf0d9d9879524c60a8278bc0716a39e5
4 schema:citation https://doi.org/10.1016/j.future.2015.03.001
5 https://doi.org/10.1016/j.sysarc.2012.07.001
6 https://doi.org/10.1109/12.543705
7 https://doi.org/10.1109/jssc.2006.870912
8 https://doi.org/10.1109/mc.2005.70
9 https://doi.org/10.1109/mcc.2014.71
10 https://doi.org/10.1109/mm.2005.104
11 https://doi.org/10.1109/tc.2004.46
12 https://doi.org/10.1109/tc.2015.2409857
13 https://doi.org/10.1109/tdmr.2005.855790
14 https://doi.org/10.1109/tns.2003.813129
15 https://doi.org/10.1109/tns.2004.840020
16 https://doi.org/10.1109/tvlsi.2013.2257902
17 schema:datePublished 2017-10
18 schema:datePublishedReg 2017-10-01
19 schema:description The shrinking feature sizes make transistors increasingly susceptible to soft errors, which can severely degrade the systems’ RAS (Reliability, Availability, and Serviceability). The tough challenge results from not only increasing SER (soft error rate) of storage cells, but also the increasing susceptibility of combinational logics to soft errors. How to efficiently detect soft errors becomes the primary problem in the Backward Error Recovery (BER) schemes that are cost-effective in soft error tolerance. This paper presents a soft error detection scheme, AUDITOR, for flip-flop based pipelines. The AUDITOR copes with both types of soft errors—single event upset (SEU) and single event transient (SET). We propose a “local-audit” fault detection mechanism, by which each pipeline stage is verified independently and the verifying result registers with a dedicated “audit” bit (V-bit). All the V-bits are distributed across the whole pipeline and synergically monitor the pipeline execution. To relax the constraint of SET detection capability imposed by the inherent fully synchronous operation mode in flip-flop based pipelines, we firstly propose using path-compensation technique to address this constraint. Furthermore, a reuse-based design paradigm is employed to reduce the implementation complexity and area overhead. The AUDITOR possesses robust detection capability and short detection latency, at the expense of about 29 % and 50 % increase in area and power consumption, respectively.
20 schema:genre research_article
21 schema:inLanguage en
22 schema:isAccessibleForFree false
23 schema:isPartOf N9287c71bad314942a76c7ccf5ea62aa2
24 Nbff9dabda01343ff8ebc4c0ab1121e63
25 sg:journal.1297359
26 schema:name A Stage-Wise Soft-Error Detection Scheme for Flip-Flop Based Pipelines in Secure Cloud Servers
27 schema:pagination 61-72
28 schema:productId N4bc1625806f74bb99c6aff3b22eb01e9
29 N56b3106664844ac19e44b73d297bf1f0
30 N7f2771f99439408fa5ab3c675232a3d7
31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016247368
32 https://doi.org/10.1007/s11265-016-1210-x
33 schema:sdDatePublished 2019-04-11T09:57
34 schema:sdLicense https://scigraph.springernature.com/explorer/license/
35 schema:sdPublisher N7819aea007cc432fa2a2bf1b080bf648
36 schema:url https://link.springer.com/10.1007%2Fs11265-016-1210-x
37 sgo:license sg:explorer/license/
38 sgo:sdDataset articles
39 rdf:type schema:ScholarlyArticle
40 N4bc1625806f74bb99c6aff3b22eb01e9 schema:name doi
41 schema:value 10.1007/s11265-016-1210-x
42 rdf:type schema:PropertyValue
43 N56b3106664844ac19e44b73d297bf1f0 schema:name readcube_id
44 schema:value 4d6a48f942163773face8010d527058b9c695b1318c039047780d35c26db0491
45 rdf:type schema:PropertyValue
46 N7819aea007cc432fa2a2bf1b080bf648 schema:name Springer Nature - SN SciGraph project
47 rdf:type schema:Organization
48 N7f2771f99439408fa5ab3c675232a3d7 schema:name dimensions_id
49 schema:value pub.1016247368
50 rdf:type schema:PropertyValue
51 N9287c71bad314942a76c7ccf5ea62aa2 schema:issueNumber 1
52 rdf:type schema:PublicationIssue
53 N9a443b760f8142ab8f2b583eb2a08caa rdf:first sg:person.011254240730.40
54 rdf:rest Ndf66adb46111445299c99eb65e5c6bbd
55 Naf0d9d9879524c60a8278bc0716a39e5 rdf:first sg:person.07661277730.08
56 rdf:rest Ncb40380685f148849f5600129388db3b
57 Nbff9dabda01343ff8ebc4c0ab1121e63 schema:volumeNumber 89
58 rdf:type schema:PublicationVolume
59 Ncb40380685f148849f5600129388db3b rdf:first sg:person.010456660330.26
60 rdf:rest N9a443b760f8142ab8f2b583eb2a08caa
61 Ndf66adb46111445299c99eb65e5c6bbd rdf:first sg:person.010402430663.17
62 rdf:rest Ndfead8adcf6345048a8b063512c213ef
63 Ndfead8adcf6345048a8b063512c213ef rdf:first sg:person.013413650033.89
64 rdf:rest rdf:nil
65 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
66 schema:name Information and Computing Sciences
67 rdf:type schema:DefinedTerm
68 anzsrc-for:0803 schema:inDefinedTermSet anzsrc-for:
69 schema:name Computer Software
70 rdf:type schema:DefinedTerm
71 sg:journal.1297359 schema:issn 0922-5773
72 1939-8115
73 schema:name Journal of Signal Processing Systems
74 rdf:type schema:Periodical
75 sg:person.010402430663.17 schema:affiliation https://www.grid.ac/institutes/grid.495262.e
76 schema:familyName Yu
77 schema:givenName Qian
78 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010402430663.17
79 rdf:type schema:Person
80 sg:person.010456660330.26 schema:affiliation https://www.grid.ac/institutes/grid.495262.e
81 schema:familyName Li
82 schema:givenName Ying
83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010456660330.26
84 rdf:type schema:Person
85 sg:person.011254240730.40 schema:affiliation https://www.grid.ac/institutes/grid.495262.e
86 schema:familyName Sun
87 schema:givenName Hongfeng
88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011254240730.40
89 rdf:type schema:Person
90 sg:person.013413650033.89 schema:affiliation https://www.grid.ac/institutes/grid.495262.e
91 schema:familyName Yang
92 schema:givenName Yanchun
93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013413650033.89
94 rdf:type schema:Person
95 sg:person.07661277730.08 schema:affiliation https://www.grid.ac/institutes/grid.495262.e
96 schema:familyName Zhang
97 schema:givenName Hong
98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07661277730.08
99 rdf:type schema:Person
100 https://doi.org/10.1016/j.future.2015.03.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009056237
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1016/j.sysarc.2012.07.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040256757
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1109/12.543705 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061088456
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1109/jssc.2006.870912 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061329393
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1109/mc.2005.70 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061387546
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1109/mcc.2014.71 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061389921
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1109/mm.2005.104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061408352
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1109/tc.2004.46 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061534014
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1109/tc.2015.2409857 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061535944
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1109/tdmr.2005.855790 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061584089
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1109/tns.2003.813129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061731883
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1109/tns.2004.840020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061732801
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1109/tvlsi.2013.2257902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061816996
125 rdf:type schema:CreativeWork
126 https://www.grid.ac/institutes/grid.495262.e schema:alternateName Shandong Women’s University
127 schema:name School of Information Technology, Shandong Women’s University, Jinan, China
128 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...