{'fraction_non_alphanumeric': 0.024514811031664963, 'fraction_numerical': 0.0, 'mean_word_length': 5.32258064516129, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A review of SNN implementation on FPGA

Spiking Neural Network (SNN), the next generation of Neural Network, is supposed to be more energy-saving than the previous generation represented by Convolution Neural Network (CNN). Although CNNs have shown impressive results on various tasks such as natural language processing, image classification, or voice recognition using Graphical Processing Units (GPUs) for training, it is expensive and is not suitable for hardware implementation. The emergence of SNNs is a solution for CNNs in terms of energy consumption. In the dozen types of hardware, Field Programmable Gate Arrays (FPGAs) is a promising approach for SNN implementation on hardware. This paper provides a survey of a number of FGPA-based SNN implementations focused on some aspects such as neuron models, network architecture, training algorithms and applications. The survey provides the reader with a compact and informative insight into recent efforts in this domain.