Novel Devices

The Physical Computation Laboratory investigates methods for exploiting knowledge of the physical properties of materials to create more efficient computation structures (e.g., AI hardware accelerators) which process data from the physical world.

Research Theme: Graphene FETs for Physics-Enhanced Machine Learning (Grappa)
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Research Theme: Graphene FETs for Physics-Enhanced Machine Learning (Grappa)
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.      
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A System for Generating Non-Uniform Random Variates Using Graphene Field-Effect Transistors
How device properties influence energy-delay metrics and the energy-efficiency of parallel computations

Phillip Stanley-Marbell, HotPower '15: Proceedings of the Workshop on Power-Aware Computing and Systems October 2015.      
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How device properties influence energy-delay metrics and the energy-efficiency of parallel computations
Tunnel Field-Effect Transistor (US Patent #8772877, Granted July 2014)
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Tunnel Field-Effect Transistor (US Patent #8772877, Granted July 2014)