Analog Computing

Analog computing leverages the continuous nature of physical systems to perform computations, aiming for significantly improved energy efficiency and speed compared to digital approaches. Current research focuses on developing robust training algorithms for analog neural networks, exploring architectures like memristive neural ODE solvers and Hopfield networks, and addressing challenges such as non-associativity and noise inherent in analog hardware. This renewed interest in analog computing is driven by the need for more energy-efficient AI and the potential for breakthroughs in areas like digital twinning, real-time signal processing (e.g., ECG analysis), and edge computing applications.