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NVIDIA and Softbank Developing AI-Based 5G MEC Telco Network

NVIDIA and Softbank recently announced that they had developed a dual-purpose AI-driven 5G MEC and vRAN distributed platform based on NVIDIA’s new GH200 Grace Hopper superchip. The two partners intend to deploy a network of regional data centres across Japan later this year to capitalize on the demand for accelerated computing and generative AI services. The shared multi-tenant platform will also offer a range of 5G vRAN applications and Softbank is creating 5G applications for autonomous driving, AI factories, augmented and virtual reality, computer vision and digital twins.

Key Takeaway No. 1: Platform Limitations

软银我s offering a dual-purpose platform where the main application is AI compute via a high-performance edge analytics platform to capitalise on the expected surge in demand for AI processing capacity. Although the company is also developing a range of 5G applications, the AI and 5G workloads will be offered simultaneously. Counterpoint Research believes that the platform is unlikely to be feasible for vRAN workloads alone and understands that the two partners are not targeting this market.

Key Takeaway No. 2: Leveraging GPU Usage in the RAN

NVIDIA GPU计划利用其处理capacity in the RAN in a number of ways, for example, to improve spectral efficiency. One way of doing this is to apply AI to optimise channel estimation feedback data between a user device and a base station. A compute intensive problem with mMIMO radios, using AI to compress receiver feedback data would reduce signalling overhead, thereby resulting in an useful increase in uplink channel capacity. This could be particularly effective at the edges of a cell. Using its GPUs in this way, NVIDIA claims that it can boost gain for cell edge users by 14-17 dB. Other applications include using AI to optimise beamforming management in millimetre wave mMIMO radios as well as to accelerate Layer 2 scheduling.

The full version of this insight report, including a complete set of Key Takeaways is published in the following report, available to clients of Counterpoint Research’s5G Network Infrastructure Service (5GNI).

New Report: NVIDIA and Softbank Join Forces To Deploy AI-Based 5G MEC Telco Network Across Japan

NVIDIA Softbank Join Forces

Table of Contents:

Snapshot
Key Highlights
– 5G MEC Telco Network
– Grace Hopper Superchip
– Leveraging Software Resources
– Performance Details
– Use Case and Deployment Options
– Key Partners
– Competitors
分析师的观点
– Platform Limitations
– Benefits of RAN-in-the-Cloud
– Eliminating RAN hardware dependency
– The Intel vs ARM battle
– Leveraging GPUs in the RAN
– A Crowded Market

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Related Reports and Blogs

New Layer-1 Accelerator Cards Set To Boost Open RAN Market – Or Create More Lock-In?

The transition of the Radio Access Network (RAN) from a standalone, integrated network into a disaggregated, virtualized solution is well underway. However, all open RAN deployments to date rely on Intel’s x86-based COTS servers, with most deployments also using Intel’s proprietary FlexRANsoftwarearchitecture. Recently, various silicon vendors have announced that they are developing alternatives to Intel’s x86 platform based on ASICs, GPUs as well RISC-V architectures. Several of these vendors are currently testing their new PCIe-based Layer-1 accelerator cards with CSPs and commercial versions of these products are expected to become widely available during the next three years.

Thisreportprovides an overview of the emerging open RAN PCIe-based Layer-1 accelerator cardmarketbased on new merchant silicon and highlights the opportunities and technical challenges facing the open RAN chip community as they strive to develop alternativechipsolutions capable of efficiently processing real-time, latency-sensitive Layer-1 workloads.

Key Takeaway No. 1: Too much diversity?

The launch of new L1 accelerator cards from various vendors, large and small, should be welcomed by CSPs calling fordiversityand will go some way to quell criticism that the openRANmarket is too Intel-based. However, CSPs may now be faced with another dilemma – too much choice! They must now face the difficult challenge of testing and comparing multiple accelerator cards, inevitably involving complicated technical and commercial trade-offs.

Key Takeaway No. 2: Look-Aside or In-Line Accelerators?

At present, the choice ofacceleratorarchitecture is binary: either look-aside or inline. Both types have their advantages and drawbacks. Depending on use cases and applications, Counterpoint Research believes thatoperatorsmay need to use both types of accelerators. However, only one vendor currently offers a software/silicon platform with the capability to do this.

Key Takeaway No. 3: Interoperability and Vendor Lock-In

Developing commercial-grade Layer 1 software suitable for massive MIMO networks is an expensive process requiring very specific skills and a lot of experience – but with no guarantee of commercial success. Although openRANis designed to promote interoperability and vendor diversity, all L1 stacks are currently tied to the underlying silicon architectures and hence are not portable between hardware platforms. This introduces a new form of vendor lock-in for CSPs. Clearly, there is an urgent need for an universal software abstraction layer between the L1 stack and the various hardware platforms to enable stack portability.

The complete versions of these Key Takeaways, including the full set of Takeaways is published in the following report, available to clients of Counterpoint Research’s5G Network Infrastructure (5GNI) Service.

Report: New L1 Accelerator Cards Set To Boost Open RAN Market – Or Create More Lock-In?

Table of Contents

  • Snapshot
  • Key Takeaways
  • Introduction
  • PCIe-based Hardware Acceleration
    • Look Aside vs In-Line Acceleration
    • Technical Trade-Offs
  • Processor Architectures
    • Types of Processors
    • Comparison of Hardware Options
    • Intel’s Xeon with vRAN Boost
  • Layer-1 Stacks
    • Reference or Commercial Grade Stacks?
    • Open or Closed Stacks?
    • Layers 2 and 3

  • Interoperability and Standardization
    • FAPI Interface
    • Proprietary L1 Software Stacks
    • Accelerator Abstraction Layer (AAL)
    • Saankya Labs RANwiser
  • Key Players (in alphabetical order)
    • AMD Xilinx
    • Dell
    • EdgeQ
    • Intel
    • Leapfrog Semiconductor
    • Marvell
    • Nvidia
    • Picocom
    • Qualcomm
  • Viewpoint

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Cloud RAN – Waiting For A Viable Business Case?

The Emerging Cloud RAN Ecosystem – Players and Solutions

Open RAN Radio Market: Product Availability Study

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