Abstract
Due to the increasing data capacity, low power consumption, and high-speed data processing expectations of systems in our daily lives today, the Von Neumann bottleneck has become a more important problem than in the past. For these reasons, conventional computer architectures can no longer fully meet today's requirements. Neuromorphic designs have been considered as an alternative solution to all, as they are able to mimic the human brain in terms of processing large amounts of data quickly with low power consumption. Although the success of traditional Artificial Neural Network methods is satisfactory, biological systems are still much more advantageous in terms of power consumption. Neuromorphic hardware architectures based on spiking neural networks, which are the most biologically plausible and are referred to as third-generation neural networks, overcome the Von Neumann bottleneck and provide a more suitable hardware structure for intelligent systems. The use of reconfigurable hardware for the implementation of neuromorphic architectures creates a faster and updatable research field than integrated circuits and computational approaches. Therefore, this study has reviewed FPGA-based reconfigurable implementations of Spiking Neural Networks in the literature and compared these studies in terms of power consumption, learning capability, resource consumption, and accuracy.