CNNs (convolutional neural networks) have been recently successfully applied for a wide range of cognitive challenges. Given high computational demands of CNNs, custom hardware accelerators are vital for boosting their performance. The high energy-efficiency, computing capabilities and reconfigurability of FPGA make it a promising platform for hardware acceleration of computation-intensive CNNs. Our research paper (attached) surveys 75+ techniques for implementing and optimizing CNN algorithms on FPGA. Accepted in Neural Computing and Applications journal 2018. We hope that this paper will attract the attention of both developers and researchers towards potential of FPGA in accelerating their applications.