Research Article
Hardware Acceleration of Computer Vision and Deep Learning Algorithms on the Edge using OpenCL
@ARTICLE{10.4108/eai.5-11-2019.162597, author={B. Mishra and D. Chakraborty and S. Makkadayil and S. D. Patil and B. Nallani}, title={Hardware Acceleration of Computer Vision and Deep Learning Algorithms on the Edge using OpenCL}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={5}, number={16}, publisher={EAI}, journal_a={CS}, year={2019}, month={11}, keywords={CNN, OpenCL, Computer Vision, Machine Learning, Industrial Automation, FPGA, OCR, Hardware Acceleration}, doi={10.4108/eai.5-11-2019.162597} }- B. Mishra
D. Chakraborty
S. Makkadayil
S. D. Patil
B. Nallani
Year: 2019
Hardware Acceleration of Computer Vision and Deep Learning Algorithms on the Edge using OpenCL
CS
EAI
DOI: 10.4108/eai.5-11-2019.162597
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.
Copyright © 2019 B. Mishra et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.