Open AccessA 15-Gbps BiCMOS XNOR gate for fast recognition of COVID-19 in binarized neural networks
Author(s) -
Rosana W. Marar,
Hazem W. Marar
Publication year - 2022
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v12i1.pp997-1002
Subject(s) - xnor gate , artificial neural network , computer science , field programmable gate array , gate array , artificial intelligence , deep learning , logic gate , computer hardware , algorithm , nand gate
The COVID-19 pandemic is spreading around the world causing more than 177 million cases and over 3.8 million deaths according to the European Centre for Disease Prevention and Control. The virus has devastating effects on economies, health, and well-being of worldwide population. Due to the high increase in daily cases, the available number of COVID-19 test kits in under-developed countries is scarce. Hence, it is vital to implement an effective screening method of patients using chest radiography since the equipment already exists. With the presence of automatic detection systems, any abnormalities in chest radiography that characterizes COVID-19 can be identified. Several artificial-intelligence algorithms have been proposed to detect the virus. However, neural networks training is considered to be time-consuming. Since computations in training neural networks are spent on floating-point multiplications, high computational power is required. Multipliers consume the most space and power among all arithmetic operators in deep neural networks. This paper proposes a 15 Gbps high-speed bipolar-complementary-metal-oxide-semiconductor (BiCMOS) exclusive-nor (XNOR) gate to replace multipliers in binarized neural networks. The proposed gate can be implemented on BiCMOS-based field-programmable gate arrays (FPGAs). This will significantly improve the response time in identifying chest abnormalities in CT scans and X-rays.