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Leveraging deep convolutional neural networks (DCNNs) for various application areas has become a recent inclination of many machine learning practitioners due to their impressive performance. Research trends show that the state-of-the-art networks are getting deeper and deeper and such networks have shown significant performance increase. Deeper and larger neural networks imply the increase in computational intensity and memory footprint. This is particularly a problem for inference-based applications on resource constrained computing platforms. On the other hand, field-programmable gate arrays (FPGAs) are becoming a promising choice in giving hardware solutions for most deep learning implementations due to their high-performance and low-power features. With the rapid formation of various state-of-the-art CNN architectures, a flexible CNN hardware processor that can handle different CNN architectures …