Neural network-based hardware classifier using CORDIC algorithm

• Published in: Modern physics letters. B, Condensed matter physics, statistical physics, applied physics Vol. 34; no. 15; p. 2050161
• Main Authors: Tiwari, Vipin, Mishra, Ashish
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 30.05.2020; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Classification; Classifiers; Datasets; Digital computers; Hardware; Machine learning; Neural networks; Programming languages; Hardware; classification; CORDIC; FPGA; neural network
• ISSN: 0217-9849; 1793-6640
• DOI: 10.1142/S0217984920501614

This paper designs a novel classification hardware framework based on neural network (NN). It utilizes COordinate Rotation DIgital Computer (CORDIC) algorithm to implement the activation function of NNs. The training was performed through software using an error back-propagation algorithm (EBPA) implemented in C++, then the final weights were loaded to the implemented hardware framework to perform classification. The hardware framework is developed in Xilinx 9.2i environment using VHDL as programming languages. Classification tests are performed on benchmark datasets obtained from UCI machine learning data repository. The results are compared with competitive classification approaches by considering the same datasets. Extensive analysis reveals that the proposed hardware framework provides more efficient results as compared to the existing classifiers.