Michael G. Xakellis

dblp:17/1973 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 80% Performance modeling and evaluation · 20%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation
hardware verification and test
0.011994
Statistical Estimation of the Switching Activity in Digital Circuits · DAC 1994
Electronic design automation
power estimation
0.011994
Statistical Estimation of the Switching Activity in Digital Circuits · DAC 1994
Performance modeling and evaluation
simulation
0.011994
Statistical Estimation of the Switching Activity in Digital Circuits · DAC 1994
Electronic design automation › circuit simulation › probabilistic simulation
statistical simulation
0.011994
Statistical Estimation of the Switching Activity in Digital Circuits · DAC 1994
Electronic design automation › power estimation
switching activity estimation
0.011994
Statistical Estimation of the Switching Activity in Digital Circuits · DAC 1994

Methods — techniques the papers use, named apart from their topics

statistical simulation · 0.0
YearPublicationVenuePosition
1994 Statistical Estimation of the Switching Activity in Digital Circuits
abstract
Abstract{Higher levels of integration have led to a generation of integrated circuits for which power dissipation and reliability are major design concerns. In CMOS circuits, both of these problems are directly related to the extent of circuit switching activity. The average number of transitions per second at a circuit node is a measure of switching activity that has been called the transition density. This paper presents a statistical simulation technique to estimate individual node transition densities. The strength of this approach is that the desired accuracy and con dence can be speci ed up-front by the user. Another key feature is the classi cation of nodes into two categories: regular- and low-density nodes. Regulardensity nodes are certi ed with user-speci ed percentage error and con dence levels. Low-density nodes are certi ed with an absolute error, with the same con dence. This speeds convergence while sacri cing percentage accuracy only on nodes which contribute little to power dissipation and have few reliability problems. I.
Michael G. Xakellis, Farid N. Najm
DAC1