Analog Computing for Energy-Efficient Machine Learning Systems

Abstract

With the exponential growth of machine learning (ML) applications, the demand for more energy-efficient systems has reached unprecedented levels. While traditional digital computing architectures have been the backbone of ML algorithms, they have become increasingly inefficient in terms of power consumption and performance, particularly when dealing with large-scale datasets and complex models. In response, analog computing has emerged as a promising solution to address these limitations, offering significant advantages in terms of energy efficiency, parallelism, and speed. Analog computing leverages continuous signals and can exploit the physical properties of hardware to perform computations in a way that digital systems cannot. This paper explores the potential of analog computing for energy-efficient ML systems, discussing its advantages, challenges, and future prospects. The key focus is on how analog computing can be integrated into current machine learning paradigms, offering a pathway toward sustainable and high-performance AI systems.