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