Error Control Coding, Second Edition
Error Control Coding, Second Edition
Nanotechnology: Science, Innovation, and Opportunity
Nanotechnology: Science, Innovation, and Opportunity
The minimum-entropy set cover problem
Theoretical Computer Science - Automata, languages and programming: Algorithms and complexity (ICALP-A 2004)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
A heterogeneous CMOS-CNT architecture utilizing novel coding of boolean functions
NANOARCH '07 Proceedings of the 2007 IEEE International Symposium on Nanoscale Architectures
Monolithic Integration of CMOS VLSI and Carbon Nanotubes for Hybrid Nanotechnology Applications
IEEE Transactions on Nanotechnology
Tanner graphs for group block codes and lattices: construction and complexity
IEEE Transactions on Information Theory
Generalized minimum distance decoding
IEEE Transactions on Information Theory
Class of algorithms for decoding block codes with channel measurement information
IEEE Transactions on Information Theory
Iterative Soft-Input Soft-Output Decoding of Reed–Solomon Codes by Adapting the Parity-Check Matrix
IEEE Transactions on Information Theory
The dynamics of group codes: state spaces, trellis diagrams, and canonical encoders
IEEE Transactions on Information Theory
Relationship Between Entropy and Test Data Compression
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Hi-index | 0.00 |
This paper determines mechanisms to mitigate errors when implementing Boolean functions in nano-circuits. Nano-fabrics are expected to have high defect rates as atomic variations directly impact such materials. This paper develops a coding mechanism that uses a combination of cheap, but unreliable nano-device as the main function and reliable, but expensive CMOS devices to implement the coding mechanism. The unique feature of this paper is that it exploits the don't-cares that naturally occur in Boolean functions to construct better codes. The reliable Boolean function problem is cast as a constraint satisfaction problem and then solved using a tree-based dynamic programming algorithm. (Here, the word "dynamic programming" is used in the same sense as computer-science literature, i.e., and as an efficient search algorithm over trees.)