NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
Concurrent entanglement routing for quantum networks: Model and designs
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NetNomos is a multi-stage framework that extracts, filters, and enforces first-order logic rules in generative ML models for networking tasks including telemetry imputation, traffic forecasting, and synthetic trace generation.
Q-GUARD achieves over 85% qualified success rate on 4-hop paths in 100-node simulations by allocating per-hop fidelity targets via Werner-state equal-split and selecting paths with a segment-local expected-goodput metric.
CCCL delivers 1.34-1.94x faster cross-node GPU collectives via CXL memory pooling than 200 Gbps InfiniBand RDMA, with 1.11x LLM training speedup and 2.75x hardware cost reduction.
CIDER improves throughput of memory-disaggregated KV stores by up to 6.6x on YCSB by replacing optimistic synchronization with pessimistic synchronization, global write-combining, and a contention-aware scheme.
NetSquid simulations characterize how memory quality, noise, distances, switches, purification and error correction affect end-to-end fidelity in entanglement-based quantum networks and yield design guidelines.
citing papers explorer
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NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
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Making Logic a First-Class Citizen in Generative ML for Networking
NetNomos is a multi-stage framework that extracts, filters, and enforces first-order logic rules in generative ML models for networking tasks including telemetry imputation, traffic forecasting, and synthetic trace generation.
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Fidelity-Guaranteed Entanglement Routing with Distributed Purification Planning
Q-GUARD achieves over 85% qualified success rate on 4-hop paths in 100-node simulations by allocating per-hop fidelity targets via Werner-state equal-split and selecting paths with a segment-local expected-goodput metric.
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CCCL: Node-Spanning GPU Collectives with CXL Memory Pooling
CCCL delivers 1.34-1.94x faster cross-node GPU collectives via CXL memory pooling than 200 Gbps InfiniBand RDMA, with 1.11x LLM training speedup and 2.75x hardware cost reduction.
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CIDER: Boosting Memory-Disaggregated Key-Value Stores with Pessimistic Synchronization
CIDER improves throughput of memory-disaggregated KV stores by up to 6.6x on YCSB by replacing optimistic synchronization with pessimistic synchronization, global write-combining, and a contention-aware scheme.
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Simulation of entanglement based quantum networks for performance characterization
NetSquid simulations characterize how memory quality, noise, distances, switches, purification and error correction affect end-to-end fidelity in entanglement-based quantum networks and yield design guidelines.