Ashwin Sanjay Lele
Papers
13
Total Citations
129
H-Index
6
About
Ashwin Sanjay Lele is pioneering the frontier of energy-efficient edge robotics, where bio-inspired intelligence meets ultra-low-power hardware. His research centers on three interconnected domains: spiking neural networks (SNNs) for robotic locomotion, event-based vision systems, and custom accelerator circuits—particularly using RRAM and FPGAs—to enable real-time autonomy on milliwatt budgets. Lele’s most impactful work, “An End-to-End Spiking Neural Network Platform for Edge Robotics” (33 citations), demonstrates how event-cameras and SNNs can replace traditional deep learning for gait adaptation in legged robots, slashing energy consumption while maintaining performance. His comprehensive survey “Robotic Computing on FPGAs” (26 citations) has become a key reference for the field, mapping challenges and opportunities in reconfigurable computing for robotics. Notably, Lele contributed to a 40nm VLIW edge accelerator with 5MB of RRAM (ISSCC 2024, 15 citations) that enables bristle-robot surveillance, showcasing his ability to push circuits from concept to silicon. By fusing neuromorphic computing with hardware-software co-design, Lele is making it possible for tiny, power-starved robots to learn, see, and move like living creatures—a critical step toward ubiquitous autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 8A Comparison of CNNs and LSTMs for EEG Signal Classification4 citations · 2022
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- 10Circuit and System Technologies for Energy-Efficient Edge Robotics3 citations · 2022