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1 October 2018 Deep alignment network: from MIMD to SIMD platform
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Proceedings Volume 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018; 1080809 (2018) https://doi.org/10.1117/12.2500268
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 2018, Wilga, Poland
Abstract
The paper considers the following software engineering problem for digital media: given a software tool for processing tensor signals, like Deep Neural network (DNN) defined for MIMD architecture (Multi Instruction, Multi Data), redefine this algorithm to SIMD architecture (Single Instruction, Multiple Data). While for mapping multiple instructions, the standard signal processing approach is applied, for mapping tensors of any dimensionality, 2D RGBA textures (Red, Green, Blue, and Alpha channels) are used as the target data structure. To illustrate the tensor mapping concept, Deep Alignment Network (DAN), contemporary important application for Human Computer Interfacing, is selected and its efficiency analyzed. The testbed for comparisons of DAN’s MIMD and SIMD architectures, was based on Javascript (MIMD) and WebGL (SIMD) software platforms. It appears that expected speed-up (checked for commodity personal computers) of SIMD versus MIMD architecture is on the reasonable level: 350 image frames per minute versus seven image frames per minute.
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Kivanc Yuksel and Władysław Skarbek "Deep alignment network: from MIMD to SIMD platform", Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 1080809 (1 October 2018); https://doi.org/10.1117/12.2500268
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