Analog Computing for Artificial Intelligence: How to Perform MAC Operations Using Ohm’s Law

White Paper Overview

The rapid proliferation of artificial intelligence (AI) systems, from voice assistants to complex computer-aided design software, has made AI an essential part of daily life. Despite its increasing ubiquity, AI’s future potential is constrained by the significant energy inefficiencies of current hardware systems. Digital AI processors, while widely used, are plagued by the energy consumption and scaling limitations inherent to digital multiplication operations. This white paper explores an innovative solution that revisits analog computing principles, leveraging Ohm's Law to perform energy-efficient multiply-accumulate (MAC) operations critical to neural network computations. We examine the potential for analog computing to revolutionize the energy landscape for AI systems and present technical considerations for implementing analog MAC operations using a novel Neuromorphic Analog Signal Processing (NASP) technology. 1. Neural networks have become a cornerstone of modern AI applications, ranging from voice recognition to industrial automation. However, the increased adoption of AI models has highlighted the limitations of current hardware, particularly in terms of energy consumption. As specialized AI chips are developed at an unprecedented rate, concerns over energy demand from data centers have risen, sparking interest in alternative solutions. Digital computing, the prevailing method for processing neural networks, presents challenges due primarily to the energy intensity of digital MAC operations. This paper proposes revisiting analog computing, a once-dismissed approach that may provide a more energy-efficient alternative to digital systems. The focus is on analog implementations of MAC operations, which are fundamental to neural networks, and how Ohm’s Law can be applied to streamline these processes.