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Early RTL delay prediction using neural networks
Daniela Sánchez Lopera
, Lorenzo Servadei
, Sebastian Prebeck
,
Wolfgang Ecker
Computation, Information and Technology
Infineon Technologies AG
Technische Universität München
Publikation
:
Beitrag in Fachzeitschrift
›
Artikel
›
Begutachtung
4
Zitate (Scopus)
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Keyphrases
Neural Network
100%
Register Transfer Level
100%
Delay Prediction
100%
Design Flow
60%
Delay Estimation
40%
Microarchitecture
40%
Training Data
20%
Computationally Expensive
20%
Different Datasets
20%
Model Performance
20%
Best Model
20%
Early Design Phases
20%
Logic Gates
20%
Level Model
20%
Coefficient of Determination
20%
Combinational Circuits
20%
Interdependency
20%
Machine Learning-based Approach
20%
Multiplexer
20%
Timing Analysis
20%
Formal Specification
20%
Static Timing Analysis
20%
Resolving Time
20%
Design Iteration
20%
Analysis-by-synthesis
20%
Chip Design
20%
Level Design
20%
S-lay
20%
Logic Synthesis
20%
Prefix Adders
20%
Digital chip
20%
Ground-truth Labels
20%
Open-source Tools
20%
Hardware Generators
20%
Computer Science
Neural Network
100%
Register-Transfer Level
100%
Microarchitecture
40%
Timing Analysis
40%
Performance Model
20%
Logic Gate
20%
Combinational Circuit
20%
Open Source Tool
20%
Early Design Stage
20%
Machine Learning-Based Approach
20%
Multiplexer
20%
Formal Specification
20%
Logic Synthesis
20%
Hardware Designer
20%
Bit Prefix Adder
20%
Training Data
20%
Interdependency
20%
Design Iteration
20%
Computer Hardware
20%
Engineering
Design Flow
100%
Delay Estimation
66%
Adders
33%
Classical Method
33%
Combinatorial Circuits
33%
Design Stage
33%
Design Iteration
33%
And Logic Gate
33%
Level Model
33%
Interdependency
33%
Logic Synthesis
33%
Multiplexer
33%
Learning System
33%