Engagement-sensitive interactive neuromuscular electrical therapy system for post-stroke balance rehabilitation - a concept study. - Deepesh Kumar; Gorish Aggarwal; Rishabh Sehgal; Abhijit Das 003; Uttama Lahiri; Anirban Dutta (NER, 2015)

Trends in Analog and Digital Intensive Compute-in-SRAM Designs. - Rishabh Sehgal; Jaydeep P. Kulkarni (AICAS, 2021)

CoMeFa: Compute-in-Memory Blocks for FPGAs. - Aman Arora 001; Tanmay Anand; Aatman Borda; Rishabh Sehgal; Bagus Hanindhito; Jaydeep Kulkarni; Lizy K. John (FCCM, 2022)

Compute-MLROM: Compute-in-Multi Level Read Only Memory for Energy Efficient Edge AI Inference Engines. - Rishabh Sehgal; Rishab Mehra; Can Ni; Jaydeep P. Kulkarni (ESSCIRC, 2023)

A Bit-Serial, Compute-in-SRAM Design Featuring Hybrid-Integrating ADCs and Input Dependent Binary Scaled Precharge Eliminating DACs for Energy-Efficient DNN Inference. - Rishabh Sehgal; Tanmay Thareja; Shanshan Xie; Can Ni; Jaydeep P. Kulkarni (IEEE J. Solid State Circuits, 2023)

CoMeFa: Deploying Compute-in-Memory on FPGAs for Deep Learning Acceleration. - Aman Arora 001; Atharva Bhamburkar; Aatman Borda; Tanmay Anand; Rishabh Sehgal; Bagus Hanindhito; Pierre-Emmanuel Gaillardon; Jaydeep Kulkarni; Lizy K. John (ACM Trans. Reconfigurable Technol. Syst., 2023)

CILP: An Arbitrary-bit Precision All-digital Compute-in-memory Solver for Integer Linear Programming Problems. - Mengtian Yang; Yipeng Wang 017; Shanshan Xie; Chieh-Pu Lo; Meizhi Wang; Sirish Oruganti; Rishabh Sehgal; Jaydeep P. Kulkarni (CICC, 2024)