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TUTCRIS

Comparing Optimization Methods of Neural Networks for Real-time Inference

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 27th European Signal Processing Conference (EUSIPCO)
KustantajaIEEE
Sivumäärä5
ISBN (elektroninen)978-9-0827-9703-9
ISBN (painettu)978-1-5386-7300-3
DOI - pysyväislinkit
TilaJulkaistu - 3 syyskuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEUROPEAN SIGNAL PROCESSING CONFERENCE -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

NimiEuropean Signal Processing Conference
ISSN (painettu)2219-5491
ISSN (elektroninen)2076-1465

Conference

ConferenceEUROPEAN SIGNAL PROCESSING CONFERENCE
Ajanjakso1/01/00 → …

Tiivistelmä

This paper compares three different optimization approaches for accelerating the inference of convolutional neural networks (CNNs). We compare the techniques of separable convolution, weight pruning, and binarization. Each method is implemented and empirically compared in three aspects: preservation of accuracy, storage requirements, and achieved speed-up. Experiments are performed both on a desktop computer and on a mobile platform using a CNN model for vehicle type classification. Our experiments show that the largest speed-up is achieved by binarization, whereas pruning achieves the largest reduction in storage requirements. Both of these approaches largely preserve the accuracy of the original network.

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