AI RESEARCH
Design Rules for Extreme-Edge Scientific Computing on AI Engines
arXiv CS.AI
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ArXi:2604.19106v1 Announce Type: cross Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and throughput requirements demand small batch sizes and require that model weights remain fully on-chip. Spatial dataflow implementations are common for extreme-edge applications. Spatial dataflow works well for small networks, but it fails to scale to larger models due to inherent resource scaling limitations.