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Andrew B. Kahng
Researcher at University of California, San Diego
Publications - 637
Citations - 25576
Andrew B. Kahng is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Routing (electronic design automation) & Integrated circuit layout. The author has an hindex of 76, co-authored 618 publications receiving 24097 citations. Previous affiliations of Andrew B. Kahng include Carnegie Mellon University & University of Michigan.
Papers
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Journal ArticleDOI
Fast spectral methods for ratio cut partitioning and clustering
L. Hagen,Andrew B. Kahng +1 more
TL;DR: It is shown that the second smallest eigenvalue of a matrix derived from the netlist gives a provably good approximation of the optimal ratio cut partition cost.
Proceedings ArticleDOI
ORION 2.0: a fast and accurate NoC power and area model for early-stage design space exploration
TL;DR: The development of ORION 2.0, an extensive enhancement of the original ORION models which includes completely new subcomponent power models, area models, as well as improved and updated technology models, confirms the need for accurate early-stage NoC power estimation.
Proceedings ArticleDOI
Cooperative mobile robotics: antecedents and directions
TL;DR: A critical survey of existing works in collective robotics is given and open problems in this field are discussed, emphasizing the various theoretical issues that arise in the study of cooperative robotics.
Journal ArticleDOI
Recent directions in netlist partitioning: a survey
TL;DR: This survey describes research directions in netlist partitioning during the past two decades in terms of both problem formulations and solution approaches, and discusses methods which combine clustering with existing algorithms (e.g., two-phase partitioning).
Proceedings ArticleDOI
Accuracy-configurable adder for approximate arithmetic designs
Andrew B. Kahng,Seokhyeong Kang +1 more
TL;DR: This paper proposes an accuracy-configurable approximate adder for which the accuracy of results is configurable during runtime, and can be used in accuracy- configurable applications, and improves the achievable tradeoff between performance/power and quality.