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dc.contributor.author
Pullini, Antonio
dc.contributor.author
Rossi, Davide
dc.contributor.author
Loi, Igor
dc.contributor.author
Tagliavini, Giuseppe
dc.contributor.author
Benini, Luca
dc.date.accessioned
2021-09-22T09:24:39Z
dc.date.available
2021-09-22T09:24:39Z
dc.date.issued
2019-07
dc.identifier.issn
0018-9200
dc.identifier.issn
1558-173X
dc.identifier.other
10.1109/JSSC.2019.2912307
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/506416
dc.description.abstract
This paper presents Mr. Wolf, a parallel ultra-low power (PULP) system on chip (SoC) featuring a hierarchical architecture with a small (12 kgates) microcontroller (MCU) class RISC-V core augmented with an autonomous IO subsystem for efficient data transfer from a wide set of peripherals. The small core can offload compute-intensive kernels to an eight-core floating-point capable of processing engine available on demand. The proposed SoC, implemented in a 40-nm LP CMOS technology, features a 108-mu W fully retentive memory (512 kB). The IO subsystem is capable of transferring up to 1.6 Gbit/s from external devices to the memory in less than 2.5 mW. The eight-core compute cluster achieves a peak performance of 850 million of 32-bit integer multiply and accumulate per second (MMAC/s) and 500 million of 32-bit floating-point multiply and accumulate per second (MFMAC/s) -1 GFlop/s-with an energy efficiency up to 15 MMAC/s/mW and 9 MFMAC/s/mW. These building blocks are supported by aggressive on-chip power conversion and management, enabling energy-proportional heterogeneous computing for always-on IoT end nodes improving performance by several orders of magnitude with respect to traditional single-core MCUs within a power envelope of 153 mW. We demonstrated the capabilities of the proposed SoC on a wide set of near-sensor processing kernels showing that Mr. Wolf can deliver performance up to 16.4 GOp/s with energy efficiency up to 274 MOp/s/mW on real-life applications, paving the way for always-on data analytics on high-bandwidth sensors at the edge of the Internet of Things.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
Digital signal processors
en_US
dc.subject
dynamic voltage scaling
en_US
dc.subject
memory architecture
en_US
dc.subject
multicore processing
en_US
dc.subject
parallel architectures
en_US
dc.title
Mr.Wolf: An Energy-Precision Scalable Parallel Ultra Low Power SoC for IoT Edge Processing
en_US
dc.type
Journal Article
dc.date.published
2019-05-15
ethz.journal.title
IEEE Journal of Solid-State Circuits
ethz.journal.volume
54
en_US
ethz.journal.issue
7
en_US
ethz.journal.abbreviated
IEEE J. Solid-State Circuits
ethz.pages.start
1970
en_US
ethz.pages.end
1981
en_US
ethz.grant
MicroLearn: Micropower Deep Learning
en_US
ethz.grant
Open Transprecision Computing
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
ethz.grant.agreementno
162524
ethz.grant.agreementno
732631
ethz.grant.fundername
SNF
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
H2020
ethz.grant.program
Projekte MINT
ethz.date.deposited
2019-07-08T05:45:52Z
ethz.source
WOS
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-09-22T09:24:47Z
ethz.rosetta.lastUpdated
2022-03-29T13:29:18Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/437239
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/351731
ethz.COinS
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