Automating Telco Network Design: A New Era of Efficiency

Summary

The telecom industry faces significant challenges in designing and managing complex networks. Manual processes and lack of standardization lead to inefficiencies and errors. NVIDIA and Infosys have collaborated to automate telco network design using NVIDIA NIM and NeMo technologies. This solution leverages generative AI to streamline the creation of TOSCA templates, enhancing productivity and reducing errors. This article explores the technical challenges and solutions behind this innovative approach.

The Challenge of Manual Network Design

Telecom wireless network design is a complex process that demands streamlined processes and standardized approaches. Network architects, engineers, and IT professionals are challenged with manually retrieving and customizing Topology and Orchestration Specification for Cloud Applications (TOSCA) templates to meet firm industry specifications. This leads to reduced productivity and increases the risk of human errors and inconsistencies in network design.

Harnessing Generative AI for Network Design

Infosys has developed an innovative solution leveraging NVIDIA’s NIM and NeMo technologies to automate the generation of TOSCA templates. This solution employs generative AI to create a standard utility capable of generating service design templates based on network engineer prompts. The automated tool, powered by NVIDIA NIM, improves the user experience by simplifying parameter edits and enabling real-time processing of user inputs to generate customized YAML templates tailored to specific TOSCA design requirements.

Technical Challenges and Solutions

To prevent delays, Infosys utilized NVIDIA GPUs to generate vector embeddings swiftly. The solution architecture included a React-based user interface, data configuration management using FAISS for efficient data handling, and robust backend services for user management and configuration. Integration with NVIDIA NIM and NeMo microservices enhanced generative AI learning and inferencing capabilities, ensuring secure authentication and authorization.

Evaluating LLM Performance

Infosys tested various LLM configurations, comparing their performance with and without NVIDIA NIM. The results demonstrated up to 28.5 lower latency and a 15 absolute improvement in accuracy using NVIDIA NIM and NeMo Retriever embedding microservices. This improved model performance enables network service designers to build network designs faster and reduce operational costs.

Sample Use Case

An example use case involves generating a TOSCA template for an Ethernet service with 100 Mbps bandwidth between 1PE and 2CE. The generative AI model responds with a service template design conforming to TOSCA standards in YAML format, showcasing the tool’s capability to produce precise and customizable templates based on user specifications.

Empowering Network Designers

By automating TOSCA template generation, Infosys’ solution addresses the time-consuming nature of manual template creation, enhancing efficiency and consistency for telecom companies. With NVIDIA NIM and NeMo technologies, network service designers can streamline workflows, boost productivity, and ensure uniformity in network design and orchestration.

Key Benefits

  • Improved Productivity: Automated TOSCA template generation reduces manual effort and errors.
  • Enhanced Accuracy: NVIDIA NIM and NeMo Retriever microservices improve model performance.
  • Streamlined Workflows: Real-time processing and simplified parameter edits enhance user experience.
  • Customizable Templates: Generative AI produces precise and customizable templates based on user specifications.

Table: Comparison of LLM Performance

Configuration Latency Accuracy
Without NVIDIA NIM Higher Lower
With NVIDIA NIM Up to 28.5 lower Up to 15 absolute improvement

Table: Key Features of Infosys’ Solution

Feature Description
Generative AI Automates TOSCA template generation based on user prompts.
NVIDIA NIM Enhances model performance and reduces latency.
NeMo Retriever Provides embedding microservices for improved accuracy.
React-based UI Simplifies user interface for parameter edits and real-time processing.
FAISS Data Management Efficient data handling for robust backend services.

Table: Sample Use Case

Service Bandwidth Endpoints Template Format
Ethernet 100 Mbps 1PE and 2CE YAML

Conclusion

The collaboration between NVIDIA and Infosys marks a significant step forward in automating telco network design. By leveraging generative AI and NVIDIA NIM and NeMo technologies, telecom companies can enhance productivity, reduce errors, and ensure uniformity in network design and orchestration. This innovative approach sets a new standard for efficiency and accuracy in the telecom industry.