👉 Relaxation computing is a class of approximate computing techniques designed to optimize the performance of complex, energy-intensive tasks by trading off some precision for significant gains in speed and efficiency. Unlike traditional computing, which aims for exact results, relaxation computing employs iterative algorithms that relax constraints to simplify problems, making them more tractable. These methods are particularly useful in scenarios such as large-scale optimization, machine learning, and scientific simulations where exact solutions are computationally prohibitive. By accepting approximate solutions that are "good enough," relaxation computing reduces computational load and energy consumption, enabling faster processing and making it a valuable tool in domains where real-time or near-real-time results are crucial.
