Accelerator research focuses on the development and integration of specialized hardware units designed to speed up specific tasks in computing systems. Unlike general-purpose CPUs, accelerators such as GPUs (Graphics Processing Units), FPGAs (Field-Programmable Gate Arrays), TPUs (Tensor Processing Units), and custom ASICs (Application-Specific Integrated Circuits) are optimized for high-performance in tasks like machine learning, data analytics, and scientific simulations. These accelerators work alongside the CPU to offload computationally intensive tasks, significantly improving processing speed and energy efficiency.

Key research areas include designing scalable accelerator architectures, optimizing data movement between accelerators and memory, and developing software frameworks that enable seamless use of these devices in heterogeneous computing systems.

As demand for AI, deep learning, and big data grows, accelerator research becomes increasingly important in driving innovation in high-performance computing and specialized application areas.