Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A design of and design tools for a novel quantum dot based microprocessor
Proceedings of the 37th Annual Design Automation Conference
Proceedings of the fourth international symposium on new phenomena in mesoscopic structures on New phenomena in mesoscopic structures
Dependency preserving probabilistic modeling of switching activity using bayesian networks
Proceedings of the 38th annual Design Automation Conference
Exploring and exploiting wire-level pipelining in emerging technologies
ISCA '01 Proceedings of the 28th annual international symposium on Computer architecture
Computational Chemistry: Applying Computational Techniques to Real-World Problems
Computational Chemistry: Applying Computational Techniques to Real-World Problems
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
Modeling Switching Activity Using Cascaded Bayesian Networks for Correlated Input Streams
ICCD '02 Proceedings of the 2002 IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD'02)
Switching activity estimation of VLSI circuits using Bayesian networks
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A Probabilistic-Based Design Methodology for Nanoscale Computation
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Designing logic circuits for probabilistic computation in the presence of noise
Proceedings of the 42nd annual Design Automation Conference
Topological parameters for time-space tradeoff
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
QCADesigner: a rapid design and Simulation tool for quantum-dot cellular automata
IEEE Transactions on Nanotechnology
IEEE Transactions on Nanotechnology
IEEE Transactions on Nanotechnology
High-resolution electron beam lithography and DNA nano-patterning for molecular QCA
IEEE Transactions on Nanotechnology
Probabilistic Modeling of QCA Circuits Using Bayesian Networks
IEEE Transactions on Nanotechnology
| Hi-index | 0.00 |
The quantum-dot cellular automata (QCA) model offers a novel nano-domain computing architecture by mapping the intended logic onto the lowest energy configuration of a collection of QCA cells, each with two possible ground states. A four-phased clocking scheme has been suggested to keep the computations at the ground state throughout the circuit. This clocking scheme, however, induces latency or delay in the transmission of information from input to output. In this paper, we study the interplay of computing error behavior with delay or latency of computation induced by the clocking scheme. Computing errors in QCA circuits can arise due to the failure of the clocking scheme to switch portions of the circuit to the ground state with change in input. Some of these non-ground states will result in output errors and some will not. The larger the size of each clocking zone, i.e., the greater the number of cells in each zone, the more the probability of computing errors. However, larger clocking zones imply faster propagation of information from input to output, i.e., reduced delay. Current QCA simulators compute just the ground state configuration of a QCA arrangement. In this paper, we offer an efficient method to compute the N-lowest energy modes of a clocked QCA circuit. We model the QCA cell arrangement in each zone using a graph-based probabilistic model, which is then transformed into a Markov tree structure defined over subsets of QCA cells. This tree structure allows us to compute the N-lowest energy configurations in an efficient manner by local message passing. We analyze the complexity of the model and show it to be polynomial in terms of the number of cells, assuming a finite neighborhood of influence for each QCA cell, which is usually the case. The overall low-energy spectrum of multiple clocking zones is constructed by concatenating the low-energy spectra of the individual clocking zones. We demonstrate how the model can be used to study the tradeoff between switching errors and clocking zones.