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Charles B. Winn

dblp:51/8314 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—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 › circuit analysis
netlist analysis
0.112010
Detecting tangled logic structures in VLSI netlists · DAC 2010
Electronic design automation
physical design
0.112010
Detecting tangled logic structures in VLSI netlists · DAC 2010
Electronic design automation › physical design
placement
0.112010
Detecting tangled logic structures in VLSI netlists · DAC 2010
Electronic design automation › physical design
circuit clustering
0.012010
Detecting tangled logic structures in VLSI netlists · DAC 2010

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

rent's rule · 0.1
YearPublicationVenuePosition
2010 Detecting tangled logic structures in VLSI netlists
abstract
This work proposes a new problem of identifying large and tangled logic structures in a synthesized netlist. Large groups of cells that are highly interconnected to each other can often create potential routing hotspots that require special placement constraints. They can also indicate problematic clumps of logic that either require resynthesis to reduce wiring demand or specialized datapath placement. At a glance, this formulation appears similar to conventional circuit clustering, but there are two important distinctions. First, we are interested in finding large groups of cells that represent entire logic structures like adders and decoders, as opposed to clusters with only a handful of cells. Second, we seek to pull out only the structures of interest, instead of assigning every cell to a cluster to reduce problem complexity. This work proposes new metrics for detecting structures based on Rent's rule that, unlike traditional cluster metrics, are able to fairly differentiate between large and small groups of cells. Next, we demonstrate how these metrics can be applied to identify structures in a netlist. Finally, our experiments demonstrate the ability to predict and alleviate routing hotspots on a real industry design using our metrics and method.
Tanuj Jindal, Charles J. Alpert, Jiang Hu 0001, Zhuo Li 0001, Gi-Joon Nam, Charles B. Winn
DAC6