Title

Convolutional neural network acceleration with hardware/software co-design

RIS ID

115880

Publication Details

A. Tzer-Yeu. Chen, M. Biglari-Abhari, K. I-Kai. Wang, A. Bouzerdoum & F. Tivive, "Convolutional neural network acceleration with hardware/software co-design," Applied Intelligence, vol. Online First, pp. 1-14, 2017.

Abstract

Convolutional Neural Networks (CNNs) have a broad range of applications, such as image processing and natural language processing. Inspired by the mammalian visual cortex, CNNs have been shown to achieve impressive results on a number of computer vision challenges, but often with large amounts of processing power and no timing restrictions. This paper presents a design methodology for accelerating CNNs using Hardware/Software Co-design techniques, in order to balance performance and flexibility, particularly for resource-constrained systems. The methodology is applied to a gender recognition case study, using an ARM processor and FPGA fabric to create an embedded system that can process facial images in real-time.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/s10489-017-1007-z