{'fraction_non_alphanumeric': 0.021573604060913704, 'fraction_numerical': 0.02030456852791878, 'mean_word_length': 5.1640625, '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}}
Extreme learning machine as a generalizable classification engine

Extreme learning machine is an emerging neural network architecture that offers fast learning and generalization for multiple tasks. In this work, a scalable digital architecture for multi-classifier extreme learning machine (MT-ELM) is proposed. The proposed architecture performs multiple classification tasks without reconfiguring the network. The design is validated with MNIST dataset and it is shown that the proposed model achieves an accuracy of 91.7% for classifying numbers in the MNIST dataset and an accuracy of 90.35% for categorizing number parity. The design is synthesized on a TSMC-65nm technology node and the power dissipation is 13.6 mW for MT-ELM network with 80 hidden neurons and 12 output neurons.