Christopher Neff

University of North Carolina at Charlotte

Papers

Total Citations

H-Index

About

Christopher Neff is a researcher focused on advancing video processing efficiency through innovative systems-level approaches. His work primarily explores compute-reuse opportunities, aiming to accelerate video analysis by identifying and eliminating redundant computations across frames. In his most-cited paper, "Measuring Compute-Reuse Opportunities for Video Processing Acceleration" (2019, 4 citations), Neff investigates how the growing bandwidth of mobile and land-line networks, which now supports high-resolution video streams, creates both challenges and opportunities for applications like robotic recognition, navigation, and security surveillance. By quantifying how often video processing tasks can be reused rather than recomputed, Neff’s research offers a pathway to significantly reduce computational load and energy consumption in real-time systems. This contribution is particularly valuable for resource-constrained devices, enabling more efficient deployment of computer vision in autonomous robots and surveillance networks. While his citation count is modest, Neff’s work addresses a foundational bottleneck in modern video processing, laying groundwork for future optimizations in latency-sensitive and power-limited environments. His research underscores the critical intersection of network evolution and algorithmic efficiency.