Single Core

"Single-core" research spans diverse fields, focusing on optimizing performance and efficiency within the constraints of a single processing unit. Current efforts involve developing algorithms and architectures for tasks like nearest-neighbor graph computation and matrix operations, aiming for speed improvements through techniques such as dynamic reconfiguration and data-locality optimization. These advancements are crucial for resource-limited applications, such as embedded systems and robotics, and also contribute to improving the efficiency of larger distributed systems by reducing communication overhead. The impact extends to various domains, including medical device development (e.g., improved needle-shape sensing) and machine learning (e.g., faster inference).