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Manoj Ragupathy

dblp:180/3737 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

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

Systems, architecture and hardware · 1

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 · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation › physical design › clock network synthesis
clock network optimization
0.212016
Flip-flop clustering by weighted K-means algorithm · DAC 2016
Electronic design automation › clustering
flip-flop clustering
0.212016
Flip-flop clustering by weighted K-means algorithm · DAC 2016
Electronic design automation
physical design
0.212016
Flip-flop clustering by weighted K-means algorithm · DAC 2016
Electronic design automation › physical design
placement
0.212016
Flip-flop clustering by weighted K-means algorithm · DAC 2016

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

weighted k-means · 0.2
YearPublicationVenuePosition
2016 Flip-flop clustering by weighted K-means algorithm
abstract
This paper presents a novel flip-flop clustering and relocation framework to help reduce the overall chip power consumption. Given an initial legalized placement, our goal is to reduce the wirelength of the clock network by reducing distance between flip-flops and their drivers, while minimize the disturbance of original placement result. The idea is to form flip-flops into clusters, such that all flip-flops within each cluster can be placed near a single clock buffer and connected by a simple routing structure. Therefore, overall clock network wirelength can be greatly reduced and significant power savings can be achieved. In particular, we propose a modified K-means algorithm which effectively assigns flops into clusters at the clustering step. Then, at the relocation step, flops are actually relocated and regularly structured clusters are formed. Our framework is evaluated on real industrial benchmarks. We compare our framework with a flow without flop clustering and an industrial window based flop clustering flow. Experimental results show our framework can achieve significant dynamic power savings while has less disturbance of the original placement.
Gang Wu 0002, Dean Wu, Manoj Ragupathy, Yu-Yen Mo, Chris C. N. Chu
DAC4