Hardware Architecture

Hardware architecture research focuses on designing efficient and powerful computing platforms for diverse applications, primarily aiming to optimize performance, energy consumption, and scalability. Current efforts concentrate on developing specialized architectures for deep learning (including novel tensor processing units and RISC-V extensions), neuromorphic computing inspired by the human brain, and in-memory computing for graph processing, alongside optimizing algorithms for efficient model training and inference. These advancements are crucial for enabling progress in fields like artificial intelligence, augmented/virtual reality, and medical imaging, where computational demands are rapidly increasing.