Hardware Acceleration of Computer Vision and Deep Learning Algorithms on the Edge using OpenCL

Mishra, B. and Chakraborty, D. and Makkadayil, S. and Patil, S. D. and Nallani, B. (2019) Hardware Acceleration of Computer Vision and Deep Learning Algorithms on the Edge using OpenCL. EAI Endorsed Transactions on Cloud Systems, 5 (16): e6. ISSN 2410-6895

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Abstract

Machine vision using CNN is a key application in Industrial automation environment, enabling real time as well as offline analytics. A lot of processing is required in real time, and in high speed environment variable latency of data transfer makes a cloud solution unreliable. There is a need for application specific hardware acceleration to process CNNs and traditional computer vision algorithms. Cost and time-to-market are critical factors in the fast moving Industrial automation segment which makes RTL based custom hardware accelerators infeasible. This work proposes a low-cost, scalable, compute-at-the-edge solution using FPGA and OpenCL. The paper proposes a methodology that can be used to accelerate traditional as well as machine learning based computer vision algorithms.

Item Type: Article
Uncontrolled Keywords: CNN, OpenCL, Computer Vision, Machine Learning, Industrial Automation, FPGA, OCR, Hardware Acceleration
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 10 Sep 2020 09:06
Last Modified: 10 Sep 2020 09:06
URI: https://eprints.eudl.eu/id/eprint/159

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