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This chapter describes describes some general concepts to consider when optimizing kernels.

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This chapter describes describes some useful optimization methods, the logic for them, and the results they provide on the test platform.

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This chapter describes some initial (as well as the simplest and most intuitive) implementations of convolution algorithms.

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Convolution operations are important in image processing, particularly in filtering. GPU compute can improve performance significantly.

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OpenVX enables embedded vision application software developers to efficiently harness the processing resources available in SoCs and systems

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This article discusses the optimization motivation, vectorization techniques and resultant performance of the FFT on ARM Mali GPUs.

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This article extends the mixed-radix FFT OpenCL implementation to two dimensions and explains optimizations for Mobile ARM Mali GPUs.

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This article analyzes the three main computation blocks of the FFT mixed-radix in a step-by-step approach, in both theory and implementation

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This article builds up the background for the 1D complex to complex FFT algorithm, pointing out the limits of DFT using direct computation.

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This white paper covers the basics of convolutional neural networks (CNNs), including a description of the various layers used.

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