dc.creator | Ríos Navarro, José Antonio | es |
dc.creator | Tapiador Morales, Ricardo | es |
dc.creator | Jiménez Moreno, Gabriel | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.date.accessioned | 2019-12-26T11:56:52Z | |
dc.date.available | 2019-12-26T11:56:52Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Rios Navarro, A., Tapiador Morales, R., Jiménez Moreno, G. y Linares Barranco, A. (2019). Efficient DMA transfers management on embedded Linux PSoC for Deep-Learning gestures recognition: Using Dynamic Vision Sensor and NullHop one-layer CNN accelerator to play RoShamBo. En Interacción 2019: XX International Conference on Human Computer Interaction Donostia, Gipuzkoa, Spain: ACM Digital Library. | |
dc.identifier.isbn | 978-1-4503-7176-6 | es |
dc.identifier.uri | https://hdl.handle.net/11441/91258 | |
dc.description.abstract | This demonstration shows a Dynamic Vision Sensor able
to capture visual motion at a speed equivalent to a highspeed
camera (20k fps). The collected visual information is presented as
normalized histogram to a CNN accelerator hardware, called
NullHop, that is able to process a pre-trained CNN to
play Roshambo against a human. The CNN designed for this
purpose consist of 5 convolutional layers and a fully connected
layer. The
latency for processing one histogram is 8ms. NullHop is deployed
on the FPGA fabric of a PSoC from Xilinx, the Zynq 7100, which
is based on a dual-core ARM computer and a Kintex-7 with 444K
logic cells, integrated in the same chip. ARM computer is running
Linux and a specific C++ controller is running the whole
demo. This controller runs at user space in order to extract the
maximum throughput thanks to an efficient use of the AXIStream,
based of
DMA transfers. This short delay needed to process one
visual histogram, allows us to average several consecutive
classification
outputs. Therefore, it provides the best estimation of the symbol
that the user presents to the visual sensor. This output is then
mapped to present the winner symbol within the 60ms latency
that the brain considers acceptable before thinking that there is a
trick. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TEC2016-77785-P | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | ACM Digital Library | es |
dc.relation.ispartof | Interacción 2019: XX International Conference on Human Computer Interaction (2019), | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep learning | es |
dc.subject | CNN | es |
dc.subject | Hardware accelerator | es |
dc.subject | FPGA | es |
dc.subject | Linux | es |
dc.subject | DMA | es |
dc.title | Efficient DMA transfers management on embedded Linux PSoC for Deep-Learning gestures recognition: Using Dynamic Vision Sensor and NullHop one-layer CNN accelerator to play RoShamBo | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | TEC2016-77785-P | es |
dc.relation.publisherversion | https://dl.acm.org/citation.cfm?id=3335597 | es |
dc.identifier.doi | 10.1145/3335595.3335597 | es |
idus.format.extent | 2 | es |
dc.eventtitle | Interacción 2019: XX International Conference on Human Computer Interaction | es |
dc.eventinstitution | Donostia, Gipuzkoa, Spain | es |
dc.relation.publicationplace | New York, USA | es |