👉 Compressed computing is a paradigm that focuses on reducing the amount of data and computational resources required to solve complex problems, particularly in scenarios where resources are limited or energy efficiency is crucial. This approach leverages various techniques such as data compression, sparse representations, and algorithmic optimizations to minimize the storage and processing demands. By compressing data, less memory is needed to store it, and by using sparse representations, algorithms can operate on fewer non-zero elements, leading to significant reductions in computational complexity and energy consumption. Compressed computing is widely applied in areas like big data analytics, machine learning, and real-time systems, where it helps to achieve better performance and scalability under resource constraints.
