Error Analysis and Evaluation of Approximate Adder Circuits in 180nm Technology

The connection between approximate computations and artificial intelligence arises from employing algorithms and methodologies as exemplars for trained models across machine learning, neural networks, reinforcement learning, and various other artificial intelligence methodologies. This paper delves into the application of approximation techniques in digital adder circuits, exploring the potential benefits in terms of energy efficiency, performance enhancement, and computational speed. By examining various types of approximate adders and their implementation strategies, the study investigates the trade-off between accuracy and efficiency. The research focuses on designing adders that achieve optimal performance while minimizing hardware overhead. Through extensive error analysis, the study sheds light on the feasibility of approximation methods in practical applications, demonstrating their effectiveness in mitigating energy and performance challenges. The findings underscore the significance of approximation techniques as a viable solution for optimizing hardware design in modern computing systems.