C. Sarah Christel

About

C. Sarah Christel is a researcher whose work sits at the intersection of computer vision and hardware acceleration, with a particular focus on deploying deep learning models on field-programmable gate arrays (FPGAs). Her most cited work, "Object recognition using FPGA and TINY YOLO" (2023), demonstrates her core contribution: bridging the gap between powerful, resource-intensive object detection algorithms and efficient, real-time hardware implementations. By adapting the lightweight TINY YOLO architecture for FPGA deployment, Christel addresses a critical challenge in embedded and edge computing—how to achieve robust object recognition without relying on high-power GPUs. This work, which has garnered 4 citations, is foundational for applications in autonomous systems, robotics, and low-power surveillance. Christel’s research is particularly valuable for students and engineers exploring practical, hardware-optimized solutions in deep learning, showing that high-performance computer vision can be achieved on compact, energy-efficient platforms. Her contributions underscore the growing importance of co-designing algorithms and hardware for next-generation intelligent systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Object recognition using FPGA and TINY YOLO

   4 citations · 2023

Key Collaborators

Contact & Links