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Network Intelligence Research Center (NIRC)
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Network Intelligence Research Center (NIRC)

NIRC is based on the National Key Laboratory of Network and Switching Technology at Beijing University of Posts and Telecommunications. It has built a technology matrix across four AI domains—intelligent cloud networking, natural language processing, computer vision, and machine learning systems—dedicated to solving real‑world problems, creating top‑tier systems, publishing high‑impact papers, and contributing significantly to the rapid advancement of China's network technology.

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Recent Articles

Latest from Network Intelligence Research Center (NIRC)

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Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jul 9, 2023 · Databases

Exploring Nebula Graph: Building Powerful Graph Database Applications

Nebula Graph is an open‑source distributed graph database that handles billions of vertices and trillions of edges with high throughput and low latency, offering a three‑service architecture, nGQL query language, installation guides, and real‑world use cases such as fraud detection, recommendation, knowledge graphs, and social networks.

Nebula Graphdistributed architecturegraph database
0 likes · 7 min read
Exploring Nebula Graph: Building Powerful Graph Database Applications
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jul 4, 2023 · Artificial Intelligence

FedMRL: Federated Meta Reinforcement Learning for Cold-Start Slice Resource Management

FedMRL tackles the cold‑start problem of network‑slice resource orchestration by combining federated learning with meta‑reinforcement learning, using a two‑loop training process that preserves SP data privacy and consistently outperforms TUNE, TDSC, and IOSP across diverse 6G network conditions.

6GFederated LearningMAML
0 likes · 6 min read
FedMRL: Federated Meta Reinforcement Learning for Cold-Start Slice Resource Management
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 27, 2023 · Artificial Intelligence

Microsecond-Scale GPU Preemption Enables Concurrent Real-Time DNN Inference

REEF introduces a reset‑based preemption mechanism and dynamic kernel padding to achieve microsecond‑scale GPU kernel preemption, enabling concurrent real‑time and best‑effort DNN inference with only 2 % added latency for real‑time tasks while boosting overall throughput by up to 7.7×, as demonstrated on the DISB benchmark.

DNN inferenceGPU schedulingREEF
0 likes · 9 min read
Microsecond-Scale GPU Preemption Enables Concurrent Real-Time DNN Inference
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 24, 2023 · Artificial Intelligence

How DFX Achieves Low-Latency Multi-FPGA Acceleration for Transformer Text Generation

The article reviews the DFX system—a multi‑FPGA server that uses model‑parallelism and a ring‑topology interconnect to accelerate GPT‑2 text generation, showing 3.78× higher throughput, 3.99× better energy efficiency, and 8.21× greater cost‑effectiveness compared with a four‑GPU V100 baseline.

FPGAGPT-2Hardware Acceleration
0 likes · 6 min read
How DFX Achieves Low-Latency Multi-FPGA Acceleration for Transformer Text Generation
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 20, 2023 · Cloud Native

Getting Started with Kubernetes: Reduce Dev Overhead and Optimize Resources

This article introduces Kubernetes, explaining its role as a container orchestration system, when it’s beneficial, how to choose an environment, manage resources with kubectl, YAML, or graphical tools, and details the control‑plane and worker‑node components that enable automated deployment, scaling, and monitoring.

Cloud NativeKubernetescontainer orchestration
0 likes · 6 min read
Getting Started with Kubernetes: Reduce Dev Overhead and Optimize Resources
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 9, 2023 · Artificial Intelligence

2023 NIRC PhD Graduates Reveal Cutting-Edge AI and Network Intelligence Research

In 2023 the Network Intelligent Research Center celebrated its largest PhD graduating class—seven scholars whose dissertations span deep‑vision hand‑gesture estimation, multi‑scenario network transmission, graph alignment, interactive streaming, knowledge‑defined networking, wireless body‑area networking, and more—showcasing significant AI‑driven advances and high‑impact publications.

Artificial IntelligenceComputer VisionGraph Alignment
0 likes · 30 min read
2023 NIRC PhD Graduates Reveal Cutting-Edge AI and Network Intelligence Research
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 5, 2023 · Artificial Intelligence

How DETR and Its Successors Evolve: A Deep Dive into the DETR Series for Object Detection

This article reviews the original DETR model, analyzes its strengths and weaknesses, and then examines two major follow‑up works—Deformable‑DETR and DAB‑DETR—explaining how they modify attention mechanisms, introduce deformable convolutions and dynamic anchor boxes to accelerate convergence and improve small‑object detection.

DAB-DETRDETRDeformable-DETR
0 likes · 12 min read
How DETR and Its Successors Evolve: A Deep Dive into the DETR Series for Object Detection
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 3, 2023 · Industry Insights

How Zhuge Uses the Shortest Congestion‑Control Loop to Achieve Consistent Low‑Latency Real‑Time Wireless Communication

The article analyzes the SIGCOMM 2022 paper introducing Zhuge, a wireless‑AP‑only solution that separates congestion feedback from the data path to shrink the control loop, dramatically cutting tail latency and boosting real‑time communication performance.

Zhugecongestion controldelay prediction
0 likes · 11 min read
How Zhuge Uses the Shortest Congestion‑Control Loop to Achieve Consistent Low‑Latency Real‑Time Wireless Communication
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
May 26, 2023 · Artificial Intelligence

IPNet: Image‑Point Cloud Network for Accurate and Robust 3D Hand Pose Estimation

IPNet introduces a hybrid Image‑Point Cloud architecture that first extracts 2D visual features with a CNN, projects them into 3D point‑cloud space, and iteratively refines hand pose using a sparse‑anchor “aggregate‑interact‑propagate” scheme, achieving state‑of‑the‑art results on challenging hand‑object datasets.

2D-3D fusionAISemGCN
0 likes · 6 min read
IPNet: Image‑Point Cloud Network for Accurate and Robust 3D Hand Pose Estimation