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Formal Techniques for Adaptive Circuit Fabrics

Formal Techniques for Adaptive Circuit Fabrics
自适应电路结构的形式技术
批准号:
0541330
负责人:
Michael Orshansky
金额:
$46.44万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-09-01 至 2011-08-31

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英文摘要
Proposal No: 0541330 Ttile: Formal Techniques for Adaptive Circuit Fabrics PI: Michael Orshansky, , University of Texas at Austin CoPI: Dennis Sylvester, University of MichiganABSTRACT:The advent of nanometer scale CMOS technology is characterized by substantial uncertainty and unpredictability of device and circuit parameters. This threatens the existing integrated circuit design paradigm by endangering the functional correctness of the designs and by reducing yield. Statistical computer-aided design tools have been recently proposed as a way to deal with variability. Much of variability, however, is systematic rather than random, including strong spatial correlations. In that case, statistical techniques may not be sufficient to maintain good yield. An alternative and orthogonal approach is the use of adaptive circuit fabrics that seek to guarantee and enforce a specified behavior in circuits even in the presence of underlying process variation. These techniques rely on sensing the process and environmental conditions and adjusting certain circuit properties to keep the design within its specifications. The objective of this research is to develop a formal approach to automated circuit synthesis onto adaptive circuit fabrics. The adaptive design paradigm for integrated circuits enforces predictable behavior through synthesizing adaptive circuit fabrics along with the target design. The desired outcome of this research is a methodology that preserves the foundations of a traditional design flow. The design synthesis tools themselves insert sufficient adaptivity to guarantee specified power, performance, and yield levels. This research will jointly develop the modeling and algorithmic aspects of adaptivity insertion, and will also explore new circuit-level techniques for adaptive behavior at low levels of granularity.
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