Analog Computing for Machine Learning: Energy and Performance Trade-offs in Neural Hardware

Abstract

Machine learning has become the driving force of modern computational systems, powering applications across natural language processing, computer vision, autonomous systems, and scientific modeling. However, the reliance on digital computing architectures for these tasks has exposed significant bottlenecks in terms of energy consumption, latency, and scalability. Analog computing has emerged as a promising alternative paradigm that leverages physical processes to perform computation in a more energy-efficient manner. In the context of machine learning, analog neural hardware has demonstrated considerable potential in achieving faster matrix multiplications, reduced memory bottlenecks, and improved energy-per-operation metrics compared to digital accelerators. This paper investigates the trade-offs between energy efficiency and computational performance in analog computing for neural hardware. Through an extensive analysis of recent experimental demonstrations and hardware prototypes, this work provides insights into the challenges and opportunities of adopting analog computing for large-scale machine learning. Results show that analog implementations can achieve up to two orders of magnitude improvements in energy efficiency, but they face challenges such as noise, precision loss, and limited programmability. Ultimately, analog computing is shown to be a viable direction for sustainable and scalable machine learning, provided that hybrid analog-digital co-design approaches are carefully integrated into future architectures.