Implementing Analog Artificial Neural Networks for Enhanced Energy Efficiency and Speed in Machine Learning Applications

This paper presents the development and simulation of an analog artificial neural network (ANN) aimed at improving energy efficiency and processing speed for machine learning tasks. We detail the architectural design of the ANN, encompassing input, hidden, and output layers, along with the incorporation of resistors and operational amplifiers as key components. Through hardware implementation, the ANN utilizes continuous voltage generators for input representation and resistance values for storing weights and biases. Our simulations demonstrate the model's accuracy and minimal error margins compared to expected outcomes. Additionally, we explore the implementation of activation functions such as ReLU and Sigmoid using specific components to further minimize energy consumption, showcasing the potential of analog ANNs in future machine learning hardware.