Heterogeneous FPGA-based Embedded Systems for Vision IoT Applications
Mostra/ Apri
Creato da
Spagnolo, Fanny
Crupi, Felice
Perri, Stefania
Corsonello, Pasquale
Metadata
Mostra tutti i dati dell'itemDescrizione
Formato
/
UNIVERSITA’ DELLA CALABRIA
Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica
Dottorato di Ricerca in
Information and Communications Technologies
CICLO XXXII; Embedded sensor devices provided by processing capabilities are opening novel and
exciting opportunities in the era of edge-computing Internet-of-Things (IoT). The workload
decentralization leads to a plenty of benefits, including better reactivity and reliability and
reduced data transfer costs. These advantages have a strong impact especially in the visual
IoT field, for which the large bandwidth required by visual data is one of the most critical
challenges. However, bringing vision technologies into smart nodes is not a trivial task,
because of the stringent energy and performance requirements, in addition to the need of
cost-effective and compact processing units. Heterogeneous architectures may represent
the key to address these necessities. Among possible heterogeneous platforms, those based
on reconfigurable devices such as Field Programmable Gate Arrays (FPGAs) show a high
adaptability to a variety of workloads, which is an important goal for edge-computing.
Therefore, their deployment in disparate IoT applications, ranging from video surveillance
to autonomous driving, is emerging as a promising solution. This dissertation proposes a
study on the suitability of modern heterogeneous FPGA System-on-Chips (SoCs) to
implement embedded smart vision sensor nodes. To this purpose, several computer vision
algorithms aimed to extract synthetic data from raw input frames have been analysed, and
novel hardware-oriented solutions have been proposed to deploy them on heterogeneous
SoCs. In all the presented cases, ranging from stereo vision to connected component
analysis and deep learning, speed performances and/or energy efficiency are considerably
improved with respect to state-of-the-art solutions. As an example, the proposed
heterogeneous architecture for convolutional neural networks achieves a power efficiency
up to 89.5% higher than competitive prior works, demonstrating its suitability in the
scenario of energy-constrained and real-time IoT.Soggetto
Heterogeneous FPGA; Computer vision; Embedded computing for IoTL; Low-power design; High-performance architectures
Relazione
ING-INF/01;