Memristor-Based Cellular Automata for Natural Language Processing
Ioannis K. Chatzipaschalis, Theodoros Panagiotis Chatzinikolaou, Iosif-Angelos Fyrigos, Andrew Adamatzky, Antonio Rubio, Georgios Ch. Sirakoulis
Abstract
Towards achieving efficient computational approaches, the perspective of Cellular Automata (CAs) appears to be highly potent, owing to its performance hidden in the parallel computing capabilities inherent in its local units, known as cells. Through their local interconnections and adherence to fixed or dynamic rules, these cells demonstrate exceptional capability in executing intricate physical events and addressing emergent problems that pose significant challenges for conventional computing systems. Capitalizing on the non-volatility and information processing capabilities offered by memristors, an oscillatory memristive CA circuit is proposed aiming to advance the field of neuromorphic computing and artificial intelligence applications, as well as to point out more effective and reliable computational models by bridging the gap between biological and circuit-based networks. In this manner, CAs are introduced as nanoelectronic memristor-based circuits to become a good candidate for utilizing Natural Language Processing (NLP) tasks like the forming of sentences.