Dataflow Architecture

Dataflow architecture focuses on optimizing the movement and processing of data within computing systems, aiming to improve efficiency and performance, particularly for computationally intensive tasks. Current research emphasizes applications in machine learning, including the acceleration of neural networks (e.g., convolutional and graph neural networks, transformers) and large language models, often employing techniques like sparsity, quantization, and specialized hardware designs (e.g., systolic arrays, FPGAs). These advancements are significant for improving the speed, energy efficiency, and scalability of AI and other data-driven applications, impacting both scientific computing and industrial deployments.