AI RESEARCH
CGRA4ML: A Hardware/Software Framework to Implement Neural Networks for Scientific Edge Computing
arXiv CS.AI
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ArXi:2408.15561v4 Announce Type: replace-cross 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.