Stelios Emmanouilidis

dblp:372/1438 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0004-9547-5320ORCID · reported

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

Systems, architecture and hardware · 1 · 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%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation › physical design › placement › circuit placement
FPGA placement
0.812024
FPGA-Placement via Quantum Annealing · FPGA 2024
Electronic design automation
physical design
0.812024
FPGA-Placement via Quantum Annealing · FPGA 2024
Emerging computing paradigms › quantum computing
quantum annealing
0.812024
FPGA-Placement via Quantum Annealing · FPGA 2024
Emerging computing paradigms
quantum computer architecture
0.812024
FPGA-Placement via Quantum Annealing · FPGA 2024
Mathematical optimization
discrete optimization
0.212024
FPGA-Placement via Quantum Annealing · FPGA 2024
Mathematical optimization › discrete optimization
quadratic unconstrained binary optimization
0.212024
FPGA-Placement via Quantum Annealing · FPGA 2024

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

quantum annealing · 1.5adiabatic quantum computing · 1.5
YearPublicationVenuePosition
2024 FPGA-Placement via Quantum Annealing
abstract
In this work we explore the use of quantum computers in solving the NP-hard placement problem of the Field-Programmable Gate Array (FPGA) implementation phase and introduce a novel approach suited for current quantum hardware sizes. Adiabatic quantum computing (AQC), with its capability to traverse expansive solution spaces, is a good fit for addressing this combinatorial problem with its exponentially large solution space. Instead of solving a single the whole problem at once, we re-formulate the placement problem as a series of so called quadratic unconstrained binary optimization (QUBO) problems which are subsequently solved via AQC. Our novel formulation facilitates a straight-forward integration of design constraints. Moreover, the size of the sub-problems can be conveniently adapted to the available hardware capabilities. Beside the sole proposal of a novel method, we ask whether contemporary quantum hardware is resilient enough to find placements for real-world-sized FPGAs. A numerical evaluation on a D-Wave Advantage 5.4 quantum annealer suggests that the answer is in the affirmative.
Thore Gerlach, Stefan Knipp, David Biesner, Stelios Emmanouilidis, Klaus Hauber, Nico Piatkowski
FPGA4