TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Resource-Constrained Implementation and Optimization of a Deep Neural Network for Vehicle Classification

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2016 24th European Signal Processing Conference (EUSIPCO)
KustantajaIEEE
Sivut1862-1866
Sivumäärä5
ISBN (elektroninen)978-0-9928-6265-7
TilaJulkaistu - syyskuuta 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEUROPEAN SIGNAL PROCESSING CONFERENCE -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2076-1465

Conference

ConferenceEUROPEAN SIGNAL PROCESSING CONFERENCE
Ajanjakso1/01/00 → …

Tiivistelmä

Deep learning has attracted great research interest in recent years in many signal processing application areas. However, investigation of deep learning implementations in highly resource-constrained contexts has been relatively unexplored due to the large computational requirements involved. In this paper, we investigate the implementation of a deep learning application for vehicle classification on multicore platforms with limited numbers of available processor cores. We apply model-based design methods based on signal processing oriented dataflow models of computation, and using the resulting dataflow representations, we apply various design optimizations to derive efficient implementations on three different multicore platforms. Using model-based design techniques throughout the design process, we demonstrate the ability to flexibly experiment with optimizing design transformations, and alternative multicore target platforms to achieve efficient implementations that are tailored to the resource constraints of these platforms.

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