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.

Domain-Specific Analog Physical Computing Accelerators
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Domain-Specific Analog Physical Computing Accelerators
Analogue-Domain Machine Learning Computations Exploiting 2D Materials Properties
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Analogue-Domain Machine Learning Computations Exploiting 2D Materials Properties
The Laplace Microarchitecture for Tracking Uncertainty and Its Implementation in a RISC-V Processor

Vasileios Tsoutsouras, Orestis Kaparounakis, Bilgesu Bilgin, Chatura Samarakoon, James Meech, Jan Heck, and Phillip Stanley-Marbell. To appear, 54th ACM/IEEE International Symposium on Microarchitecture (MICRO-54).      
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The Laplace Microarchitecture for Tracking Uncertainty and Its Implementation in a RISC-V Processor
Efficient Programmable Random Variate Generation Accelerator from Sensor Noise

James T. Meech and Phillip Stanley-Marbell, IEEE Embedded Systems Letters.      
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Efficient Programmable Random Variate Generation Accelerator from Sensor Noise