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MB-CNN: Memristive Binary Convolutional Neural Networks for Embedded Mobile Devices

1
School of Computing, University of Utah, Salt Lake City, UT 84112, USA
2
Electrical & Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Low Power Electron. Appl. 2018, 8(4), 38; https://doi.org/10.3390/jlpea8040038
Received: 1 May 2018 / Revised: 11 October 2018 / Accepted: 11 October 2018 / Published: 13 October 2018
(This article belongs to the Special Issue Energy-Aware Neuromorphic Hardware)
Applications of neural networks have gained significant importance in embedded mobile devices and Internet of Things (IoT) nodes. In particular, convolutional neural networks have emerged as one of the most powerful techniques in computer vision, speech recognition, and AI applications that can improve the mobile user experience. However, satisfying all power and performance requirements of such low power devices is a significant challenge. Recent work has shown that binarizing a neural network can significantly improve the memory requirements of mobile devices at the cost of minor loss in accuracy. This paper proposes MB-CNN, a memristive accelerator for binary convolutional neural networks that perform XNOR convolution in-situ novel 2R memristive data blocks to improve power, performance, and memory requirements of embedded mobile devices. The proposed accelerator achieves at least 13.26 × , 5.91 × , and 3.18 × improvements in the system energy efficiency (computed by energy × delay) over the state-of-the-art software, GPU, and PIM architectures, respectively. The solution architecture which integrates CPU, GPU and MB-CNN outperforms every other configuration in terms of system energy and execution time. View Full-Text
Keywords: convolutional neural networks; binary convolutions; in-situ processing; RRAM technology; computer architecture; embedded systems convolutional neural networks; binary convolutions; in-situ processing; RRAM technology; computer architecture; embedded systems
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Pal Chowdhury, A.; Kulkarni, P.; Nazm Bojnordi, M. MB-CNN: Memristive Binary Convolutional Neural Networks for Embedded Mobile Devices. J. Low Power Electron. Appl. 2018, 8, 38.

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