Physics-aware training for the physical machine learning model building
Published in The Innovation, 2022
In recent decades, machine learning has emerged as a very powerful computational method. Because of its exceptional successes in computer science and engineering, machine learning has ignited research interest in other disciplines, including biology, chemistry, physics, and finance. Machine learning models, which are usually regarded as mathematical models, have traditionally been implemented on the basis of digital computing platform. The increasing prevalence of machine learning has been accompanied by a rapid increase of computing requirements, outpacing Moore’s law. Therefore, researchers have been committed to the development of analog computing hardware platforms to overcome the inherent limitations of computing resources. Considering that wave physics is an attractive candidate to build analog processor, wave-based analog computing platforms are emerging as an important direction to implement machine learning. Most wave-based analog processors are designed on the basis of the mathematical isomorphism between physical systems and conventional machine learning models, such as deep neural networks (DNNs), implying that analog processors can be trained using standard training techniques for neural networks.
