activity
20142021
most citedSelf-similar Magneto-electric Nanocircuit Technology for Probabilistic Inference Engines

38 citations · 75 across the 9 of their papers we have counts for

collaborators

9 papers

cs.DC2021

Efficient State-space Exploration in Massively Parallel Simulation Based Inference

Sourabh Kulkarni, Csaba Andras Moritz

Simulation-based Inference (SBI) is a widely used set of algorithms to learn the parameters of complex scientific simulation models. While primarily run on CPUs in HPC clusters, th…

cs.DC2020★ 9 cited

Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19

Sourabh Kulkarni, Mario Michael Krell, Seth Nabarro +1

Epidemiology models are central in understanding and controlling large scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian…

cs.ET2016★ 5 cited

Routability in 3D IC Design: Monolithic 3D vs. Skybridge 3D CMOS

Jiajun Shi, Mingyu Li, Santosh Khasanvis +2

Conventional 2D CMOS technology is reaching fundamental scaling limits, and interconnect bottleneck is dominating integrated circuit (IC) power and performance. While 3D IC technol…

cs.ET2016★ 1 cited

Skybridge-3D-CMOS: A Vertically-Composed Fine-Grained 3D CMOS Integrated Circuit Technology

Mingyu Li, Jiajun Shi, Mostafizur Rahman +3

Parallel and monolithic 3D integration directions offer pathways to realize 3D integrated circuits (ICs) but still lead to layer-by-layer implementations, each functional layer bei…

cs.OH2015★ 1 cited

Manufacturing Pathway and Experimental Demonstration for Nanoscale Fine-Grained 3-D Integrated Circuit Fabric

Mostafizur Rahman, Jiajun Shi, Mingyu Li +2

At sub-20nm technologies CMOS scaling faces severe challenges primarily due to fundamental device scaling limitations, interconnection overhead and complex manufacturing. Migration…

cs.ET2015★ 38 cited

Self-similar Magneto-electric Nanocircuit Technology for Probabilistic Inference Engines

Santosh Khasanvis, Mingyu Li, Mostafizur Rahman +5

Probabilistic graphical models are powerful mathematical formalisms for machine learning and reasoning under uncertainty that are widely used for cognitive computing. However they…