Abstract

In the era of emerging technologies, the demand for analog circuits that are both power-efficient and high-performing has increased significantly. Traditional methods of analog circuit design often struggle to meet the stringent requirements of modern applications such as high-speed communications, IoT devices, and wearable technology. The primary challenge lies in balancing power consumption with performance metrics like speed and accuracy, especially as devices scale down in size and operate at lower voltages. To address this, we propose an AI-driven approach to analog circuit design, leveraging machine learning algorithms and optimization techniques to automate the design process and achieve optimal power-speed trade-offs. Our method utilizes reinforcement learning (RL) combined with genetic algorithms (GA) to explore the vast design space of analog circuits. These AI techniques iteratively improve the circuit design by evaluating performance against multiple objectives such as power consumption, speed, and reliability. The RL model continuously refines the design parameters, while the GA assists in identifying the most promising design candidates. This hybrid approach offers an efficient solution for tackling complex analog circuit design problems in emerging technologies. The outcomes of our approach show significant improvements in both power efficiency and speed performance when compared to conventional design methods. Using a set of benchmark circuit designs, we show the ability of the AI-driven model to optimize designs for specific application requirements.

Authors

Yogita Deepak Mane, Neeta P. Patil
Thakur College of Engineering and Technology, India

Keywords

AI-Driven Design, Low Power, High-Speed Performance, Analog Circuits, Emerging Technologies