👉 LD computing, or Low-Density Parity-Check computing, is a specialized form of computation designed for handling large-scale linear algebra operations, particularly in machine learning and deep learning applications. It leverages the sparsity inherent in many real-world datasets and models, where most elements are zero or close to zero. By focusing on non-zero elements only, LD computing significantly reduces memory usage and computational complexity compared to traditional methods, which store and process all elements, even the zeros. This approach enables efficient matrix-vector multiplications and other operations crucial in neural networks, making it a powerful tool for accelerating training and inference processes while maintaining high performance.
