S. Vijayashaarathi

Papers

Total Citations

H-Index

About

S. Vijayashaarathi is a researcher at the forefront of embedded computer vision, specializing in the intersection of deep learning and hardware acceleration. Their most impactful work, "Object recognition using FPGA and TINY YOLO" (2023, 4 citations), demonstrates a practical approach to deploying real-time object detection on resource-constrained devices. By implementing the lightweight TINY YOLO architecture on Field-Programmable Gate Arrays (FPGAs), Vijayashaarathi addresses a critical challenge in edge computing: achieving high-performance computer vision without relying on power-hungry GPUs. This contribution is particularly significant for applications in autonomous systems, robotics, and IoT, where low latency and energy efficiency are paramount. Vijayashaarathi’s research bridges the gap between algorithmic advances in deep learning and their tangible deployment in hardware, showcasing how FPGAs can serve as viable platforms for real-time object recognition. Their work not only highlights the synergy between computer vision and hardware design but also provides a scalable blueprint for integrating AI into embedded systems. With a focus on making object recognition accessible and efficient, Vijayashaarathi continues to push the boundaries of practical AI, inspiring students and researchers to explore the transformative potential of hardware-accelerated machine learning.