Repository logo
 
Publication

Stochastic theater: stochastic datapath generation framework for fault-tolerant IoT sensors

dc.contributor.authorDuarte, Rui Policarpo
dc.contributor.authorVéstias, Mário
dc.contributor.authorCarvalho, Carlos
dc.contributor.authorCasaleiro, João
dc.date.accessioned2019-01-02T11:17:41Z
dc.date.available2019-01-02T11:17:41Z
dc.date.issued2018
dc.description.abstractStochastic Computing has emerged as a competitive computing paradigm that produces fast and simple implementations of arithmetic operations, while offering high levels of parallelism, and graceful degradation of the results when in the presence of errors. IoT devices are often operate under limited power and area constraints and subjected to harsh environments, for which, traditional computing paradigms struggle to provide high availability and fault-tolerance. Stochastic Computing is based on the computation of pseudo-random sequences of bits, hence requiring only a single bit per signal, rather than a data-bus. Notwithstanding, we haven’t witnessed its inclusion in custom computing systems. In this direction, this work presents Stochastic Theater, a framework to specify, simulate, and test Stochastic Datapaths to perform computations using stochastic bitstreams targeting IoT systems. In virtue of the granularity of the bitstreams, the bit-level specification of circuits, high-performance characteristics and reconfigurable capabilities, FPGAs were adopted to implement and test such systems. The proposed framework creates Stochastic Machines from a set of user defined arithmetic expressions, and then tests them with the corresponding input values and specific fault injection patterns. Besides the support to create autonomous Stochastic Computing systems, the presented framework also provides generation of stochastic units, being able to produce estimates on performance, resources and power. A demonstration is presented targeting KLT, typical method for data compression in IoT applications.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationDUARTE, Rui Policarpo; [et al] – Stochastic theater: stochastic datapath generation framework for fault-tolerant IoT sensors. i-ETC: ISEL Academic Journal of Electronics, Telecommunications and Computers. ISSN 2182-4010. Vol. 4, N.º 1 (2018), pp. 1-9pt_PT
dc.identifier.issn2182-4010
dc.identifier.urihttp://hdl.handle.net/10400.21/9244
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherISELpt_PT
dc.relationPTDC/EEI-HAC/31819/2017 - FCTpt_PT
dc.subjectIoTpt_PT
dc.subjectFPGApt_PT
dc.subjectFault-tolerant computingpt_PT
dc.subjectStochastic bitstreamspt_PT
dc.subjectApproximate computingpt_PT
dc.subjectComputação aproximadapt_PT
dc.titleStochastic theater: stochastic datapath generation framework for fault-tolerant IoT sensorspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F50021%2F2013/PT
oaire.citation.endPage9pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titlei-ETC: ISEL Academic Journal of Electronics, Telecommunications and Computerspt_PT
oaire.citation.volume4pt_PT
oaire.fundingStream5876
person.familyNameVéstias
person.familyNameCasaleiro
person.givenNameMário
person.givenNameJoão
person.identifier.ciencia-id4717-C2C7-3F2C
person.identifier.ciencia-id101D-E2CA-5F74
person.identifier.orcid0000-0001-8556-4507
person.identifier.orcid0000-0002-4722-0954
person.identifier.ridH-9953-2012
person.identifier.scopus-author-id14525867300
person.identifier.scopus-author-id35241828400
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationa7d22b29-c961-45ac-bc09-cd5e1002f1e8
relation.isAuthorOfPublication15646b2c-e3fb-4cbf-8293-1770ba8420e3
relation.isAuthorOfPublication.latestForDiscoverya7d22b29-c961-45ac-bc09-cd5e1002f1e8
relation.isProjectOfPublication9964a800-3334-42d6-aab0-1f8870cbe7b1
relation.isProjectOfPublication.latestForDiscovery9964a800-3334-42d6-aab0-1f8870cbe7b1

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Stochastic.pdf
Size:
1.04 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: