Research

Research Interests

Computer Vision

Artificial Intelligence

Embedded Systems

Research Area

Energy-efficient embedded vision applications

Ongoing Research

Software Hardware Approach for Ultra-Low Power Embedded Vision

Deep Neural Networks (DNNs) have become highly popular choice for embedded vision applications because of the accuracy and versatility. In addition to the end node inference, incremental learning or continual learning are getting popular for the vision hardware due to online learning capabilities. Although a high number of research has been carried out to optimize DNN models, executing DNN models for both inference and learning on the edge devices is still a major challenge due to power hungry heavy computation and memory bandwidth requirements. The focus of this research is to propose novel software-hardware approaches for ultra low power DNN inference and edge learning deployment in vision end nodes.

Poster Presentations

Baby, B. E. & Das, S. Software Hardware Approach for Ultra-Low Power Embedded Vision. Poster presented at Research Scholar’s Day (RSD 2022); 27 Jan - 28 Jan 2023; IIT Palakkad.

Baby, B. E. & Das, S. Ultra-Low Power Software Hardware Approach for Embedded Vision. Poster presented at Industry Academia Conclave (IAC 4.0);13 Aug 2022; IIT Palakkad.

Baby, B. E. & Das, S. On-Board Hyperspectral Image Processing. Poster presented at 2021 ACM Europe Summer School on HPC Computer Architectures for AI and Dedicated Applications; 30 Aug - 3 Sep 2021; Barcelona, Spain.