Analog Domain

Analog domain computing leverages the physics of analog circuits to perform computations, primarily aiming for increased energy efficiency and speed compared to digital approaches, particularly for machine learning tasks. Current research focuses on optimizing analog neural networks, including those based on superconducting circuits and resistive networks, and mitigating challenges like noise and non-associativity through techniques such as improved activation functions and careful circuit design and partitioning. This field holds significant promise for enabling energy-efficient and potentially faster inference and training of deep learning models in resource-constrained environments like mobile devices and embedded systems.