Journal of Research in Electrical Circuits and Systems ISSN: 3139-2059 (Online)

Authors: Prof. Ravikumar S. Naik, Dr. Pankaj K. Sharma

Abstract: Artificial neural networks (ANNs) have become cornerstones in modern computation, powering applications from pattern recognition to autonomous systems. Traditionally realized in digital hardware or software simulators, neural networks face challenges in energy consumption, speed, and scalability. Analog circuits offer an alternative path, capitalizing on the continuous nature of physical signals to implement neural computations directly in hardware. This paper explores analog neural network implementations, detailing foundational concepts, circuit architectures, learning mechanisms, and practical challenges. Through analysis and comparisons with digital systems, analog neural networks reveal opportunities for ultra–low power and high throughput computing, particularly in edge devices and real–time signal processing. The paper includes design examples, tables comparing key architectures, 2D figures illustrating core circuit blocks, and an extensive reference list of original works.

Keywords: Analog neural networks, Operational transconductance amplifiers, Memristive synapses, Learning circuits, Neuromorphic analog design