Matrix Multiplication

Matrix multiplication, a fundamental operation in linear algebra, is central to numerous scientific and engineering applications, particularly in machine learning where it forms the backbone of many model architectures. Current research focuses on optimizing matrix multiplication for efficiency and accuracy, particularly within deep learning models like transformers and large language models, exploring techniques such as low-precision quantization, algorithmic improvements (e.g., Strassen's algorithm and its variants), and hardware acceleration using GPUs, FPGAs, and even specialized optical processors. These advancements are crucial for enabling the deployment of increasingly complex models while mitigating the computational and energy costs associated with their training and inference.