Measurement Uncertainty

The Physical Computation Laboratory investigates hardware structures for efficiently quantifying uncertainty in sensors as well as hardware architectures for tracking uncertainty throughout computations.

Research Theme: Uncertainty In Measurement and Computation (Laplace)
Members Public

Research Theme: Uncertainty In Measurement and Computation (Laplace)
Drap: A Python Library for Representing and Computing with Arbitrary Empirical Probability Distributions

Chatura Samarakoon, Bilgesu Bilgin, Orestis Kaparounakis, Vasileios Tsoutsouras, and Phillip Stanley-Marbell. Under review, ACM Transactions on Mathematical Software (TOMS).      
Members Public

Drap: A Python Library for Representing and Computing with Arbitrary Empirical Probability Distributions
A System for Generating Non-Uniform Random Variates Using Graphene Field-Effect Transistors

N. J. Tye, J. T. Meech, B. A. Bilgin, and P. Stanley-Marbell, 31st IEEE International Conference on Application-specific Systems, Architectures and Processors, July 2020.      
Members Public

A System for Generating Non-Uniform Random Variates Using Graphene Field-Effect Transistors
Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms

P. Stanley-Marbell, A. Alaghi, M. Carbin, E. Darulova, L. Dolecek, A. Gerstlauer, G. Gillani, D. Jevdjic, T. Moreau, M. Cacciotti, A. Daglis, N. Enright Jerger, B. Falsafi, A. Misailovic, A. Sampson, and D. Zufferey, ACM Computing Surveys, 2020.      
Members Public

Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms