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Chimera: Neuro-Symbolic Attention Primitives for Trustworthy Dataplane Intelligence
arXiv – CS AI|Rong Fu, Xiaowen Ma, Kun Liu, Wangyu Wu, Ziyu Kong, Jia Yee Tan, Tailong Luo, Xianda Li, Zeli Su, Youjin Wang, Yongtai Liu, Simon Fong|
🤖AI Summary
Chimera introduces a framework that enables neural network inference directly on programmable network switches by combining attention mechanisms with symbolic constraints. The system achieves line-rate, low-latency traffic analysis while maintaining predictable behavior within hardware limitations of commodity programmable switches.
Key Takeaways
- →Chimera maps neural attention computations onto dataplane primitives for real-time network traffic analysis.
- →The framework uses kernelized, linearized attention approximation with key-selection hierarchy to work within hardware constraints.
- →A cascade fusion mechanism enforces symbolic guarantees while preserving neural network expressivity.
- →The system includes hardware-aware mapping and two-timescale updates for stable line-rate operation.
- →Empirical results show high-fidelity inference is possible within resource limits of commodity programmable switches.
#neural-networks#attention-mechanisms#dataplane#network-infrastructure#programmable-switches#real-time-inference#neuro-symbolic#hardware-optimization
Read Original →via arXiv – CS AI
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