Anupreetham Anupreetham

About

Anupreetham Anupreetham is a researcher at the forefront of efficient computer vision and hardware acceleration, with a primary focus on deploying deep learning models for real-time object detection. His most impactful work introduces an end-to-end FPGA-based architecture that pipelines a convolutional neural network (CNN) with non-maximum suppression, enabling single-shot detectors to achieve high-speed, low-latency inference directly on hardware. This contribution, published in 2021 and garnering 16 citations, addresses critical bottlenecks in autonomous driving, smart surveillance, and robotics by moving beyond software-only solutions. By optimizing both the CNN feature extraction and the post-processing pipeline for reconfigurable logic, Anupreetham’s research demonstrates how to balance accuracy and throughput in resource-constrained environments. His work stands out for its practical, system-level approach—bridging algorithm design and hardware implementation—making deep learning more accessible for edge applications. For students and researchers exploring the intersection of computer vision and embedded systems, Anupreetham’s contributions offer a compelling blueprint for building efficient, real-world AI systems.

Research Focus

Key Achievements

Top Papers

1. 1
   End-to-End FPGA-based Object Detection Using Pipelined CNN and Non-Maximum Suppression

   16 citations · 2021

Key Collaborators

Contact & Links