Quantum Cellular Automata

We are interested in Low Energy Computing where computing occurs due to the coupling of the computing elements. In conventional computing, electrons flow from one points to another to process information. In Nano-computing-Resaerch-Group@EE-Univ. of South florida, we pursue a novel cellular automata computing paradigm, where state of the computing elements change and the next computing element couples to the change in the previous computing element and extreme low power dissipation is possible. In Quantum Cellular Automata, each computing cell has four Q-dot and two electrons. Electrons occupy the diagonal Q-dots to minimize the overall energy. Inter-cell barrier confines the electrons in the cell. However, electrons in the neighboring cells allign themselves accroding to the driver cell transfering information. Various cells including a shift register, inverter etc are fabricated and large designs are have been proposed using four phase clocking scheme. We are interested in modeling reliablity, defect, design and power-error trade-offs in Quantum Cellular Automata.

 

Download the QCAPro tool

Reliability Analysis for post-CMOS Devices

We work on probabilistic graph structures like Bayesian Networks to model statistical variability of nano-devices. Note that this problem is not new and huge literature exists on"computing with unreliable components" and bounds.

What changed though from before is the phenomenal error rates simply due to extremely low energy computing requirements that random thermal energy can cause temporary errors. We transform the circuit (shown in Figure) into a probabilistic network which in turn is transformed into a junction tree for local computing advantages.

Problems that we intend to look at are

1. Can we arrive at models driven by the underlying Physics of the devices?
2. What would be best heuristics to track worst case scenario?
3. Error/Defect at the boundaries of integration of various devices.
4. Are we heading back to analog? If so, why not use some of the strength?
5. Can we learn structures successfully in inputs and in defects?