Spatial Accelerator

Spatial accelerators are specialized hardware designed to significantly speed up computationally intensive tasks by dedicating separate processing units to different parts of a problem, unlike traditional temporal architectures that reuse units. Current research focuses on accelerating various applications, including deep learning (e.g., Graph Neural Networks, Large Language Models), scientific computing (e.g., solving partial differential equations), and signal processing, employing techniques like decoupled computation, hash-based mapping, and dataflow architectures. These advancements aim to overcome limitations of existing hardware in terms of speed, energy efficiency, and reproducibility, thereby impacting fields ranging from AI and scientific simulations to high-performance computing.