Publication Type : Conference Paper
Publisher : IEEE
Source : 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Coimbatore, India (2020)
Url : https://ieeexplore.ieee.org/document/9074447
Keywords : Biological neural networks, Brain on a-board, Communication systems, computational modeling, Hardware, Machine learning, Neural networks, Rectified Linear Unit Function, Resistance, Sigmoid Function, Software
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2020
Abstract : In an era of digital computing and software supremacy, analogue computing and electronics often take a back seat. And it is only fair to say that current computers, general applications digital devices, are relied on because of their efficient software rather than hardware. The current flaws in combination with new age computing such as machine learning, and the limitations become glaring especially on devices on the lower end. To overcome this, it becomes necessary to rely on analogue techniques to allow efficient computation and fulfilling the need for dedicated Machine Learning devices capable of similar accuracy as software models. In this paper, with the goal of developing analogue neural networks, the accuracy obtained for on board sensor data was 77% same as a software approach.
Cite this Research Publication : B. Bhardwaj, S. R., D., Anjali, S., Ganesan, M., and Dr. Lavanya R., “Brain on a-board implementation of Neural Networks”, in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020.