Design Rules for Extreme-Edge Scientific Computing on AI Engines
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. AI Engines on modern FPGA SoCs offer a promising alternative with high compute density and additional on-chip memory. However, the architecture, programming model, and performance-scaling behavior of AI Engines differ fundamentally from thos...
| Authors | Zhenghua Ma, G Abarajithan, Dimitrios Danopoulos et al. |
|---|---|
| Primary category | cs.AR |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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