Sparse Format

Sparse formats are data structures designed to efficiently represent matrices and tensors with a high proportion of zero values, a common characteristic in many large-scale machine learning models. Current research focuses on developing novel sparse formats optimized for specific applications, such as large language model inference and spiking neural network simulation, often incorporating techniques like compression and tailored code generation for improved performance on diverse hardware architectures. These advancements aim to reduce memory consumption and accelerate computations, thereby enabling the training and deployment of larger and more complex models while addressing the limitations of existing general-purpose sparse formats.