Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic...

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" summary

Implements a prototype on P4 Barefoot Tofino and claims line‑speed neural inference by running a binarized RNN with sliding‑window recurrence entirely on the data plane while offloading only ambiguous flows to an off‑switch Transformer via DPDK+GPU. System escalates under 5% of flows, classifies over 95% of flows on‑switch, and reports average F1 gains of 133% and 31% versus NetBeacon and NN‑on‑NIC (up to 177%/39% on harder tasks). Uses ~20% SRAM and ~1% TCAM, and end‑to‑end IMS latency stays under two seconds for typical loads.

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" chapters (9)

1. 0:00Brain-on-Switch project
2. 1:19Traditional traffic analysis limits
3. 3:06RNNs on switch motivation
4. 5:14Brain-on-Switch design goals
5. 6:15On-switch RNN architecture
6. 8:47Ambiguous flow escalation
7. 10:15Off-switch Transformer inference
8. 11:00Experimental evaluation results
9. 13:10Conclusion and future

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