Aravind Dasu
Papers
1
Total Citations
5
H-Index
1
About
Aravind Dasu’s research lies at the intersection of reconfigurable computing, embedded systems, and algorithm-hardware co-design, with a focus on efficient implementations for robotics and signal processing. His most-cited work, “A Faddeev Systolic Array for EKF-SLAM and its Arithmetic Data Representation Impact on FPGA” (2017), addresses the critical challenge of real-time simultaneous localization and mapping (SLAM) by leveraging systolic array architectures on FPGAs. This paper explores how data representation—such as fixed-point versus floating-point arithmetic—affects both accuracy and resource utilization, offering practical insights for deploying complex estimation algorithms on resource-constrained hardware. While his citation count is modest, Dasu’s contributions are notable for their engineering depth, bridging theoretical algorithm design with tangible hardware implementation. His work is particularly relevant for researchers in autonomous systems, where low-latency, power-efficient computation is paramount. Dasu’s approach exemplifies a hands-on, systems-level perspective, making his research a valuable reference for those seeking to optimize robotic perception pipelines through custom digital logic.
Research Focus
Key Achievements
Top Papers
- 1