Sumedh R. Risbud
Papers
2
Total Citations
25
H-Index
2
About
Sumedh R. Risbud is a leading researcher at the intersection of neuromorphic computing and real-time control systems, with a focus on energy-efficient AI hardware. His work centers on implementing advanced algorithms on event-driven neuromorphic processors, such as Intel’s Loihi 2, to achieve dramatic gains in speed and power efficiency. Risbud’s most cited paper, “Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor” (2023, 18 citations), demonstrates how sparse coding can be executed on neuromorphic chips with unprecedented energy savings, laying the groundwork for scalable, brain-inspired computing. His more recent contribution, “Neuromorphic Quadratic Programming for Efficient and Scalable Model Predictive Control” (2024, 7 citations), pioneers the use of neuromorphic architectures to solve large optimization problems for robotic control in size-, weight-, and power-constrained (SWaP) environments. This work promises to revolutionize autonomous systems at the edge, enabling real-time decision-making with minimal energy consumption. Risbud’s research is pivotal for advancing next-generation robotics and embedded AI, bridging the gap between theoretical neuromorphic models and practical, deployable solutions.
Research Focus
Key Achievements
Top Papers
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