Research Focus
At IIIT Bangalore, my research revolves around hardware-software co-design frameworks that facilitate the deployment of advanced AI algorithms on specialized hardware. Key areas of focus include:
Multi-Agent Deep Reinforcement Learning (DRL): Architecting heterogeneous computing systems that enable multiple AI agents to learn collaboratively on FPGA-accelerated platforms, reducing latency and improving decision-making speed.
Energy-Efficient AI Inference: Designing novel FPGA and ASIC accelerators optimized for convolutional neural networks (CNNs) and other deep learning models, enabling real-time processing within stringent power budgets.
Edge AI System Integration: Developing modular hardware platforms integrated with AI frameworks, enabling seamless deployment of machine learning models on embedded devices for applications ranging from robotics to environmental sensing.
Notable Projects
FPGA-Based Convolutional Neural Network Accelerator for Agriculture: A real-time system that processes aerial images captured by drones to detect crop diseases and nutrient deficiencies with high accuracy and minimal energy consumption.
Heterogeneous Computing Framework for Multi-Agent DRL: A pioneering platform combining CPUs, GPUs, and FPGAs to accelerate collaborative learning and inference for swarm robotics applications.
Autonomous Drone Navigation: Implementing embedded vision algorithms on FPGA hardware to enable obstacle detection and path planning in dynamic environments.
Collaborations & Funding
My research is supported by grants from the Government of Karnataka Center of Excellence and involves close collaboration with industry partners and academic institutions globally. This network facilitates the translation of research innovations into practical, scalable technologies.