Shilpa Mayannavar
Papers
2
Total Citations
7
H-Index
2
About
Dr. Shilpa Mayannavar is a researcher at the forefront of explainable artificial intelligence and neuromorphic hardware design. Her primary research areas span image recognition, neural network architecture innovation, and hardware acceleration for AI applications. Dr. Mayannavar’s most notable contribution is the development of the Auto Resonance Network (ARN), a novel feed-forward hierarchical architecture that is both approximating and explainable—a significant departure from traditional black-box neural networks. Her seminal 2019 paper, "A Noise Tolerant Auto Resonance Network for Image Recognition," has garnered 5 citations and established the foundational principles of ARN’s noise resilience. Building on this, her 2024 work, "Design and Implementation of Hardware Accelerators for Neural Processing Applications," directly addresses the practical deployment of ARN for robotic motion planning by creating dedicated hardware accelerators. This work bridges the critical gap between theoretical AI models and real-time, energy-efficient hardware implementation. With her focus on transparent, robust architectures and their physical realization, Dr. Mayannavar is paving the way for more trustworthy and deployable AI systems in robotics and beyond.
Research Focus
Key Achievements
Top Papers
- 1A Noise Tolerant Auto Resonance Network for Image Recognition5 citations · 2019
- 2