Near Data Processing (NDP) / Processing in Memory (PIM) research focuses on minimizing the data movement between memory and processors, which is a major bottleneck in modern computing systems. By bringing computation closer to the data—either within or near the memory itself—this approach reduces latency, improves energy efficiency, and boosts overall system performance.

Key areas of study include designing memory architectures that integrate processing units directly into memory (PIM) and developing algorithms that can leverage this architecture for efficient data processing. NDP and PIM are particularly useful for data-intensive applications like machine learning, big data analytics, and real-time processing, where the cost of moving data often outweighs computation.

This research is critical as it addresses the limitations of traditional von Neumann architectures, opening up new possibilities for scalable, high-performance computing systems.