Journal
BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES
Volume 66, Issue 6, Pages -Publisher
POLSKA AKAD NAUK, POLISH ACAD SCI, DIV IV TECHNICAL SCIENCES PAS
DOI: 10.24425/bpas.2018.125927
Keywords
convolutional neural networks; resource-efficient computation; algorithm optimization
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Funding
- Ministry of Education and Science of the Russian Federation [14.756.31.0001]
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Convolutional neural networks (CNN) have become ubiquitous in computer vision as well as several other domains, but the sheer size of the modern CNNs means that for the majority of practical applications, a significant speed up and compression are often required. Speeding-up CNNs therefore have become a very active area of research with multiple diverse research directions pursued by many groups in academia and industry. In this short survey, we cover several research directions for speeding up CNNs that have become popular recently. Specifically, we cover approaches based on tensor decompositions, weight quantization, weight pruning, and teacher-student approaches. We also review CNN architectures designed for optimal speed and briefly consider automatic architecture search.
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