Name
Title | ||
|---|---|---|
Projection-based statistical analysis of full-chip leakage power with non-log-normal distributions |
Abstract | ||
|---|---|---|
In this paper we propose a novel projection-based algorithm to estimate the full-chip leakage power with consideration of both inter-die and intra-die process variations. Unlike many traditional approaches that rely on log-Normal approximations, the proposed algorithm applies a novel projection method to extract a low-rank quadratic model of the logarithm of the full-chip leakage current and, therefore, is not limited to log-Normal distributions. By exploring the underlying sparse structure of the problem, an efficient algorithm is developed to extract the non-log-Normal leakage distribution with linear computational complexity in circuit size. In addition, an incremental analysis algorithm is proposed to quickly update the leakage distribution after changes to a circuit are made. Our numerical examples in a commercial 90nm CMOS process demonstrate that the proposed algorithm provides 4x error reduction compared with the previously proposed log-Normal approximations, while achieving orders of magnitude more efficiency than a Monte Carlo analysis with 104 samples. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1146909.1146941 | DAC |
Keywords | Field | DocType |
incremental analysis algorithm,cmos process,non-log-normal leakage distribution,projection-based statistical analysis,log-normal approximation,non-log-normal distribution,monte carlo analysis,leakage distribution,efficient algorithm,proposed algorithm,full-chip leakage power,novel projection-based algorithm,cmos integrated circuits,process variation,projection method,computational complexity,chip,algorithms,integrated circuit design,leakage current,log normal distribution,statistical analysis,statistics,monte carlo methods | Monte Carlo method,Leakage (electronics),Computer science,Electronic engineering,Projection method,CMOS,Integrated circuit design,Logarithm,Log-normal distribution,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
0738-100X | 1-59593-381-6 | 24 |
PageRank | References | Authors |
1.30 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Li | 1 | 709 | 48.36 |
| Jiayong Le | 2 | 275 | 18.31 |
| Lawrence Pileggi | 3 | 358 | 31.47 |