CGRA4ML: A Hardware/Software Framework to Implement Neural Networks for Scientific Edge Computing

📰 ArXiv cs.AI

CGRa4ML is a hardware/software framework for implementing neural networks in scientific edge computing

advanced Published 30 Mar 2026

Action Steps

Design and generate parameterized accelerators using CGRa4ML's modular framework
Implement neural networks on the generated accelerators for scientific edge computing
Integrate the accelerators into edge computing applications using software frameworks
Verify and validate the performance of the neural networks on the accelerators

Who Needs to Know This

ML engineers and researchers on a team can benefit from CGRa4ML as it provides a modular framework for generating parameterized accelerators, while software engineers can utilize it for integrating ML models into edge computing applications

Key Insight

💡 CGRa4ML provides a modular and open-source framework for generating parameterized accelerators for neural networks in edge computing

Key Takeaways

CGRa4ML is a hardware/software framework for implementing neural networks in scientific edge computing

Full Article

Title: CGRA4ML: A Hardware/Software Framework to Implement Neural Networks for Scientific Edge Computing

Abstract:
arXiv:2408.15561v4 Announce Type: replace-cross Abstract: The scientific community increasingly relies on machine learning (ML) for near-sensor processing, leveraging its strengths in tasks such as pattern recognition, anomaly detection, and real-time decision-making. These deployments demand accelerators that combine extremely high performance with programmability, ease of integration, and straightforward verification. We present cgra4ml, an open-source, modular framework that generates paramet

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