Digital VLSI and FPGA Architectures
Overview
Our research is centered on designing ASIC (Application-Specific Integrated Circuit) and FPGA (Field-Programmable Gate Array) architectures for low-power, memory-efficient computing. These designs are tailored for a wide range of applications, including biomedical devices, artificial intelligence, and quantum computing. By optimizing power consumption and memory usage, we aim to enhance the performance and efficiency of computational systems across these cutting-edge fields.
Raman Architecture
Selected Publications
- Gautam, P. K., Kalipatnapu, S., Singhal, U., Lienhard, B., Singh, V., & Thakur, C. S. (2024). RLow-latency machine learning FPGA accelerator for multi-qubit-state discrimination. arXiv preprint arXiv:2407.03852.
- Krishna, A., Nudurupati, S. R., Chandana, D. G., Dwivedi, P., van Schaik, A., Mehendale, M., & Thakur, C. S. (2024). Raman: A re-configurable and sparse tinyML accelerator for inference on edge. IEEE Internet of Things Journal.
- Nair, A. R., Nath, P. K., Chakrabartty, S., & Thakur, C. S. (2022). Multiplierless MP-kernel machine for energy-efficient edge devices. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 30(11), 1601-1614.
- Nair, A. R., Chakrabartty, S., & Thakur, C. S. (2021). In-filter computing for designing ultralight acoustic pattern recognizers. IEEE Internet of Things Journal, 9(8), 6095-6106.
- Krishna, A., Van Schaik, A., & Thakur, C. S. (2021). FPGA implementation of particle filters for robotic source localization. IEEE Access, 9, 98185-98203.
- Krishna, A., Mittal, D., Virupaksha, S. G., Nair, A. R., Narayanan, R., & Thakur, C. S. (2021). Biomimetic FPGA-based spatial navigation model with grid cells and place cells. Neural Networks, 139, 45-63.
- Xu, Y., Afshar, S., Wang, R., Cohen, G., Singh Thakur, C., Hamilton, T. J., & van Schaik, A. (2021). A biologically inspired sound localisation system using a silicon cochlea pair. Applied Sciences, 11(4), 1519.
- Gupta, S., Chakraborty, S., & Thakur, C. S. (2021). Neuromorphic time-multiplexed reservoir computing with on-the-fly weight generation for edge devices. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2676-2685.