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14 February 2020 OpenCL-code generation framework for MobileNets with depthwise separable convolution and merged layers
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Proceedings Volume 11431, MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging; 1143108 (2020) https://doi.org/10.1117/12.2541926
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China
Abstract
Deep convolutional neural networks are increasingly used in various parallel embedded platforms such as mobile GPUs, AMD APUs, and FPGAs. At the same time, many new models have been developed for embedded platforms, such as MobileNet. In order to balance accuracy, speed and resource requirements and achieve cross-platform versatility, we have developed a software framework for in-depth research. Generated an OpenCL code that takes full advantage of parallel resources and improves the parallel efficiency of OpenCL code. Another advantage is that it optimizes and consolidates the network and compiles offline, making the entire application most efficient. MobileNets uses nonlongitudinal separable convolution (deep separable convolution) instead of standard convolution. Experiments with MobileNet have shown that the OpenCL code generation framework can significantly improve the efficiency of use.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bin Zhou, Songzhi Jin, Yanxin Liu, Mengxi Hao, and Hui Zhang "OpenCL-code generation framework for MobileNets with depthwise separable convolution and merged layers", Proc. SPIE 11431, MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging, 1143108 (14 February 2020); https://doi.org/10.1117/12.2541926
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