S
Saibal Mukhopadhyay
Researcher at Georgia Institute of Technology
Publications - Â 432
Citations - Â 10232
Saibal Mukhopadhyay is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Computer science & CMOS. The author has an hindex of 40, co-authored 381 publications receiving 8814 citations. Previous affiliations of Saibal Mukhopadhyay include IBM & Purdue University.
Papers
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Journal ArticleDOI
A Memory-Based Logic Block With Optimized-for-Read SRAM for Energy-Efficient Reconfigurable Computing Fabric
TL;DR: A memory-based logic block (MLB) is presented in 130-nm CMOS with an optimized-for-read 6T static random access memory (SRAM)-based lookup table and demonstrates single- and multicycle evaluation of complex functions.
Posted Content
Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity
TL;DR: A novel stochastic STDP algorithm that uses spiking frequency information to dynamically adjust synaptic behavior is presented that is tested in pattern recognition task with noisy input and shows accuracy improvement over deterministic STDP.
Proceedings ArticleDOI
Thermal mangament of multicore processors using power multiplexing
TL;DR: It is observed that the selection of appropriate migration policy and the migration rate can efficiently reduce the spatial non-uniformity and peak temperature on the chip.
Proceedings ArticleDOI
INVITED: RTL-to-GDS Tool Flow and Design-for-Test Solutions for Monolithic 3D ICs
Heechun Park,Kyungwook Chang,Bon Woong Ku,Jinwoo Kim,Edward Lee,Dae Hyun Kim,Arjun Chaudhuri,Sanmitra Banerjee,Saibal Mukhopadhyay,Krishnendu Chakrabarty,Sung Kyu Lim +10 more
TL;DR: This paper presents a thorough RTL-to-GDS design flow for monolithic 3D IC, which is based on commercial 2D place-and-route (P&R) tools and clever ways to extend them to handle 3DIC designs and simulations, and provides a low-cost built-in-self-test (BIST) method to detect various faults that can occur on ILVs.
Journal ArticleDOI
Design and Rationale of an intelligent algorithm to Detect BuRnoUt in HeaLthcare Workers in COVID Era using ECG and artificiaL Intelligence: The BRUCEE-LI study
Mohit Gupta,Ankit Bansal,Prattay Guha Sarkar,M.P. Girish,Manish K. Jha,Jamal Yusuf,Suresh Kumar,Satish Kumar,Ajeet Jain,Sanjeev Kathuria,Rajni Saijpaul,Anurag Mishra,Vikas Malhotra,Rakesh Yadav,Sivasubramanian Ramakrishanan,Rajeev Malhotra,Vishal Batra,Manu Kumar Shetty,Nandini Sharma,Saibal Mukhopadhyay,Sandeep Garg,Anubha Gupta +21 more
TL;DR: It is proposed that ECG data generated from the people with burnout can be utilized to develop AI-enabled model to predict the presence of stress and burnout in HCWs in COVID-19 era.