*Result*: Inversion schemes for sublithographic programmable logic arrays.

Title:
Inversion schemes for sublithographic programmable logic arrays.
Authors:
Gojman, B.1, Manem, H.2, Rose, G. S.2, DeHon, A.3 andre@seas.upenn.edu
Source:
IET Computers & Digital Techniques (Institution of Engineering & Technology). Nov2009, Vol. 3 Issue 6, p625-642. 18p. 9 Diagrams, 7 Charts, 2 Graphs.
Database:
Business Source Premier

*Further Information*

*A programmable logic array (PLA) needs its inputs available in both the positive and negative polarities. In lithographic-scale VLSI PLAs, programmable array logics (PALs) and programmable logic devices (PLDs) a buffer and inverter at the PLA input typically produces both polarities from a single polarity input. However, the extreme regularity required for sublithographic designs has driven nanoscale architectures to consider alternate solutions. Consequently, the authors compare three schemes: one based on producing both polarities in a restoration stage (selective inversion), one based on a local inversion stage and one based on a full dual-rail logic implementation. The authors develop a mapping flow for the dual-rail logic and quantify its cost in both logical product terms and physical implementation area and also develop area and timing models for all three schemes. Mapping benchmarks from the Toronto 20 set, the authors are able to show that the local inversion scheme is faster (less than one-fifth the latency), lower energy (one-half the energy) and comparable size to the selective inversion scheme and faster (less than half the latency), smaller (one-third of the area) and lower energy (one-ninth the energy) than the dual-rail scheme. [ABSTRACT FROM AUTHOR]

Copyright of IET Computers & Digital Techniques (Institution of Engineering & Technology) is the property of Institution of Engineering & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)*