Accelerating OpenVX Application Kernels Using Halide Scheduling

Journal of Signal Processing Systems(2023)

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摘要
In this study, we investigate how to use a Domain-Specific Language—Halide to accelerate and optimize OpenVX graphs. Halide is a new high-level image processing pipeline language. It offers developers to separate the program into algorithms and schedule. This makes developers program friendly. The Halide image processing language has also proven to be an effective system for authoring high-performance image processing code. We present a prototype that use Halide to optimize OpenVX image processing modules. Since OpenVX is a lack of scheduling primitives, but Halide does. We implemented Halide into OpenVX graphs. This method increases the developer’s utilities and achieves relatively high performance.
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关键词
OpenVX, Halide, Image processing, Convolutional neural networks
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