Niklas Jungnitz

dblp:389/7024 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0007-3601-7890ORCID · reported

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

Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

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 · 50% Emerging computing paradigms · 50%

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

TopicWeightPapersLastEvidence papers
Emerging computing paradigms › approximate computing
approximate circuit design
0.812024
SAS - A Framework for Symmetry-based Approximate Synthesis · DAC 2024
Emerging computing paradigms
approximate computing
0.812024
SAS - A Framework for Symmetry-based Approximate Synthesis · DAC 2024
Electronic design automation › logic synthesis › logic optimization
approximate logic synthesis
0.812024
SAS - A Framework for Symmetry-based Approximate Synthesis · DAC 2024
Electronic design automation
logic synthesis
0.812024
SAS - A Framework for Symmetry-based Approximate Synthesis · DAC 2024

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

hamming distance minimization · 0.8boolean function approximation · 0.8
YearPublicationVenuePosition
2024 SAS - A Framework for Symmetry-based Approximate Synthesis
abstract
Approximate Computing is a design paradigm that trades off computational accuracy for gains in non-functional aspects such as reduced area, increased computation speed, or power reduction. The latter is of special interest in the field of Internet of Things. In this paper we present SAS, a framework for symmetry-based approximate logic synthesis. Given a Boolean multi-output function, SAS approximates it by (partially) replacing its output functions by symmetric functions with minimal Hamming distance. The framework is capable of restricting the introduced error with respect to a parameterized error metric that covers many real-word use-cases.
Niklas Jungnitz, Oliver Keszöcze
DAC1