graph neural networks

Towards trustworthy system latency optimization of Positron Emission Tomography with RBC and GNN

Positron Emission Tomography (PET) is a highly sensitive imaging instrument, and its system latency plays a pivotal role in real-world applications. We propose a novel trustworthy optimization method to reduce PET system latency by leveraging Role-Based Collaboration (RBC) and Graph Neural Networks (GNN). First, we est...

Consistent Vulnerability Detection Using Graph Neural Networks

Artificial Intelligence in Electronic Design Automation: A Survey on Logic Synthesis and Netlist Optimization

Modern digital integrated circuits contain very large numbers of logic gates, and the conventional heuristics used in logic synthesis and netlist optimization struggle to explore the resulting design space efficiently. Artificial Intelligence (AI) and Machine Learning (ML) have therefore been investigated as complement...

Artificial Intelligence in Electronic Design Automation: A Survey on Logic Synthesis and Netlist Optimization

Modern digital integrated circuits contain very large numbers of logic gates, and the conventional heuristics used in logic synthesis and netlist optimization struggle to explore the resulting design space efficiently. Artificial Intelligence (AI) and Machine Learning (ML) have therefore been investigated as complement...

An AI-Assisted Approach to Arithmetic Logic Unit (ALU) Design Optimization for Enhanced Processor Performance and Efficiency

This research explores how Artificial Intelligence can enhance the optimization of Arithmetic Logic Units (ALUs), which are crucial for processor speed, energy efficiency, and silicon area. Traditional ALU design relies on fixed architectures and rule-based heuristics, but the proposed framework integrates Graph Neural...

Artificial Intelligence-Assisted Design for Enhanced Performance and Energy Efficiency in Computer Architecture

This research journal discusses how Artificial Intelligence (AI) and Machine Learning (ML) can enhance computer architecture design by addressing challenges in performance and energy efficiency. Traditional methods rely on expert intuition and exhaustive simulations, which are limited when exploring vast design spaces....

An AI-Assisted Approach to Arithmetic Logic Unit (ALU) Design Optimization for Enhanced Processor Performance and Efficiency

This research explores how Artificial Intelligence can enhance the optimization of Arithmetic Logic Units (ALUs), which are crucial for processor speed, energy efficiency, and silicon area. Traditional ALU design relies on fixed architectures and rule-based heuristics, but the proposed framework integrates Graph Neural...

Artificial Intelligence-Assisted Design for Enhanced Performance and Energy Efficiency in Computer Architecture

This research journal discusses how Artificial Intelligence (AI) and Machine Learning (ML) can enhance computer architecture design by addressing challenges in performance and energy efficiency. Traditional methods rely on expert intuition and exhaustive simulations, which are limited when exploring vast design spaces....

INTELLIGENT CAD/CAM INTEGRATION FOR ADAPTIVE MANUFACTURING: A SYSTEMATIC REVIEW OF AI AND IOT FRAMEWORKS IN INDUSTRY 4.0

The union of Artificial Intelligence and the Internet of Things (IoT) is transforming the way Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems are used in the Industry 4.0 production process. Based on the PRISMA 2020 reporting framework (Page et al., 2021), this report is a systematic literatu...

Quantization fragility under image corruption is recipe-dependent: paired evidence from INT8 and FP8 object detectors: Reproducibility Package

Reproducibility package accompanying the manuscript Quantization fragility under image corruption is recipe-dependent: paired evidence from INT8 and FP8 object detectors, prepared for Neural Networks. The release contains analysis, pipeline, and validation code; frozen configurations and manifests; compact deterministi...

Identifying core genes and exploring the omnigenic architecture of complex traits using interpretable graph neural networks

Understanding the relationship between phenotype and genotype is a central challenge of 21st century biology. Although rare, strong mutations allow for a direct understanding of this relationship, modern genetics is puzzled by the finding that complex traits are influenced by hundreds or thousands of variants, often wi...

KG-Orchestrator Graph Neural Network-Driven Resource Orchestration for 6G Distributed Networks

The highly distributed infrastructure and the hetero geneous services and dynamic workloads of future 6G networks impose severe requirements on resource orchestration. We in troduce KG-Orchestrator, a unified framework which combines a Neo4j knowledge graph and a multi-GNN ensemble (Graph SAGE, HashGNN, GAT, GCN) to pe...