Carlo C. Del Mundo

Papers

Total Citations

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About

Carlo C. Del Mundo is a computer scientist whose research lies at the intersection of data-intensive algorithms and hardware-software co-design, with a focus on accelerating machine learning and database workloads. His most cited work, "NCAM: Near-Data Processing for Nearest Neighbor Search" (2016), addresses a critical bottleneck in modern AI systems: the data movement overhead in k-nearest neighbor (kNN) search, a fundamental algorithm used across natural language processing, computer vision, and robotics. By proposing a near-data processing architecture that brings computation closer to memory, Del Mundo’s work demonstrates how to significantly reduce latency and improve throughput for these memory-bound operations. Though his citation count is modest, his contributions are notable for tackling a pervasive challenge in deploying efficient AI at scale—bridging the gap between algorithmic demands and hardware limitations. His research is particularly relevant for students and engineers working on edge computing, real-time systems, or energy-efficient accelerators, as it highlights the importance of rethinking traditional computing hierarchies to meet the needs of modern data-driven applications.