Overview
We are a leading research group advancing the frontiers of event-based sensing and neuromorphic technologies. Our work spans hardware design, hardware-software co-design, algorithm development, and end-to-end system integration. We focus on cutting-edge areas such as neuromorphic cameras, neuromorphic radar, and neuromorphic brain-computer interfaces, with the goal of building next-generation intelligent systems that are fast, efficient, and adaptive.
Our research has led to pioneering contributions across diverse fields including astronomy, autonomous driving, robotics, microscopy and neuroscience. Notable achievements include developing high dynamic range (HDR) techniques for celestial imaging, unlocking new possibilities in astronomical observation, and introducing innovative algorithms for asynchronous noise filtering, segmentation, anomaly detection, and unsupervised event-data representation.
We are committed to shaping the future of low-latency, energy-efficient sensing and computation, bringing neuromorphic technologies closer to real-world applications and societal impact.
Demo: Neuromorphic Radar
The following video showcases the real-time operation of a neuromorphic radar system developed at our lab. This radar leverages event-based sensing to detect motion and depth with ultra-low latency and power consumption.
Selected Publications
- Yadav, S. S., Roy, N., & Thakur, C. S. ENHANCING CELESTIAL IMAGING: HIGH DYNAMIC RANGE WITH NEUROMORPHIC DETECTORS.
- Yadav, S. S., Pradhan, B., Ajudiya, K. R., Kumar, T. S., Roy, N., Van Schaik, A., & Thakur, C. S. (2025). Neuromorphic Cameras in Astronomy: Unveiling the Future of Celestial Imaging Beyond Conventional Limits. arXiv preprint arXiv:2503.15883.
- Mangalwedhekar, R., Singh, N., Thakur, C. S., Seelamantula, C. S., Jose, M., & Nair, D. (2023). Achieving nanoscale precision using neuromorphic localization microscopy. Nature Nanotechnology, 18(4), 380-389.
- Annamalai, L., & Thakur, C. S. (2024). EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm. arXiv preprint arXiv:2402.10078.
- Annamalai, L., Ramanathan, V., & Thakur, C. S. (2024). EventMASK: A Frame-Free Rapid Human Instance Segmentation with Event Camera Through Constrained Mask Propagation. IEEE Robotics and Automation Letters.
- Annamalai, L., & Thakur, C. S. (2024). EventASEG: An Event-Based Asynchronous Segmentation of Road With Likelihood Attention. IEEE Robotics and Automation Letters.
- Kudithipudi, D., Schuman, C., Vineyard, C. M., Pandit, T., Merkel, C., Kubendran, R., ... & Furber, S. (2025). Neuromorphic computing at scale. Nature, 637(8047), 801-812.
- Annamalai, L., Ramanathan, V., & Thakur, C. S. (2022). Event-LSTM: An unsupervised and asynchronous learning-based representation for event-based data. IEEE Robotics and Automation Letters, 7(2), 4678-4685.
- Annamalai, L., Chakraborty, A., & Thakur, C. S. (2021). EvAn: neuromorphic event-based sparse anomaly detection. Frontiers in Neuroscience, 15, 699003.
- Thakur, C. S., Wang, R., Hamilton, T. J., Tapson, J., & van Schaik, A. (2016). A low power trainable neuromorphic integrated circuit that is tolerant to device mismatch. IEEE Transactions on Circuits and Systems I: Regular Papers, 63(2), 211-221.
- Pradhan, B. R., Bethi, Y., Narayanan, S., Chakraborty, A., & Thakur, C. S. (2019, May). N-HAR: A neuromorphic event-based human activity recognition system using memory surfaces. In 2019 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.