Traditional DNN quantization sacrifices accuracy—our LUT-based distillation doesn’t. By mapping neurons to flow tables, we deploy complex DNNs in P4 switches with zero information loss. Demonstrated on UNSW-NB15 dataset, it hits 93% F1-score for DDoS detection. 🌟 Follow us on…
Hardware P4 switches lack ALUs and fast stateful memory, making DNN deployment impossible—until now. Our cascaded LUT distillation maps any DNN into lookup tables, enabling wirespeed inference with no performance loss. Tested on DDoS mitigation, it achieves 93% F1-score with…
Retraining at the edge reduces latency but limits computational power; retraining in the cloud offers scalability but increases propagation delay. Our predictive approach balances both, ensuring minimal SLA violations while optimizing resource use. 🌟 Follow us on LinkedIn! Check…
The Zero-Touch Network and Service Management (ZSM) framework is realized through O-RAN, where Non-RT and Near-RT RICs host AI/ML models. Our predictive retraining integrates seamlessly, using LSTM and LOF to keep models accurate and efficient in dynamic 6G environments. 🌟…
Detecting new traffic patterns in B5G networks requires unsupervised anomaly detection. We compared OC-SVM, Isolation Forest, and LOF—LOF won with 99% accuracy, enabling predictive retraining that adapts to dynamic QoS demands. 🌟 Follow us on LinkedIn! Check the updates…
Retraining AI/ML models on fixed thresholds or periodic schedules leads to SLA violations or unnecessary computational costs. Our predictive method adapts to real-time traffic changes, using LSTM and LOF to retrain only when needed—cutting violations by 40%. 🌟 Follow us…
Static retraining schedules fail in dynamic B5G networks. Our predictive approach uses unsupervised classifiers (e.g., Local Outlier Factor) to detect new traffic patterns and trigger just-in-time retraining, reducing SLA violations and resource waste. 🌟 Follow us on LinkedIn! Check…
This week, we advanced 6G MIMO innovation and GPGPU virtualization, proving that smarter networks and faster computing are within reach. Here’s what stood out: 📡 6G MIMO Breakthroughs 💻 GPGPU Virtualization Revolution 🌍 Join the Revolution Explore our research and…
Solving data placement as an integer linear program (ILP) is precise but slow. CATER combines ILP for global optimality and heuristics for real-time adaptability, reducing computation time by 90% while keeping constraint violations at 0%. 🌟 Follow us on LinkedIn!…
Cloud storage optimizes for scalability and availability—edge storage must also handle real-time constraints like GDPR, hardware specs, and data collocation. CATER delivers optimal placement with 23% fewer nodes, proven on Apache Ozone in real-world edge scenarios. 🌟 Follow us on…
