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pro vyhledávání: '"Kapoor, Parichay"'
Autor:
Ham, MyungJoo, Moon, Jijoong, Lim, Geunsik, Jung, Jaeyun, Ahn, Hyoungjoo, Song, Wook, Woo, Sangjung, Kapoor, Parichay, Chae, Dongju, Jang, Gichan, Ahn, Yongjoo, Lee, Jihoon
We propose NNStreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to deep neural network applications. A new trend with the wide-spread of deep neural network applications is
Externí odkaz:
http://arxiv.org/abs/2101.06371
Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model size reduction, achieving performance enhancement on devices is, however
Externí odkaz:
http://arxiv.org/abs/1905.10138
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs). Computations using sparse matrices obtained by pruning parameters, however, exhibit vastly different parallelism depending on
Externí odkaz:
http://arxiv.org/abs/1905.05686
Model compression has been introduced to reduce the required hardware resources while maintaining the model accuracy. Lots of techniques for model compression, such as pruning, quantization, and low-rank approximation, have been suggested along with
Externí odkaz:
http://arxiv.org/abs/1810.12823
Autor:
Geraci, James R., Kapoor, Parichay
Convolutional Neural Network (CNN) recognition rates drop in the presence of noise. We demonstrate a novel method of counteracting this drop in recognition rate by adjusting the biases of the neurons in the convolutional layers according to the noise
Externí odkaz:
http://arxiv.org/abs/1702.00932
Modern consumer electronic devices have adopted deep learning-based intelligence services for their key features. Vendors have recently started to execute intelligence services on devices to preserve personal data in devices, reduce network and cloud
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::914624153c80ec8a965d7618c95240e9
http://arxiv.org/abs/2206.04688
http://arxiv.org/abs/2206.04688