APPLICATION INFRASTRUCTURE

Anuket, an open source project to accelerate compliance with infrastructure, interoperability and 5G deployments, is being launched by LF Networking.

prnewswire | January 27, 2021

Anuket, an open source project to accelerate compliance with infrastructure, interoperability and 5G deployments, is being launched by LF Networking.
LF Networking (LFN), which encourages joint effort and operational greatness across open source organizing projects, today declared the making of the Anuket project. A consolidation of CNTT (The Cloud iNfrastructure Telco Taskforce, which gave reference models and structures to both virtual machine and containerized network capacities) and OPNFV (the Open Platform for NFV, which diminished chance to coordinate and convey NFV framework and installed VNF/CNFs), Anuket apparatuses and antiquities engage the worldwide interchanges local area to convey agreeable organization benefits quicker, more dependably and safely, and quicken the change to cloud local foundations.

Anuket conveys normalized reference foundation details and conformance structures for virtualized and cloud local organization capacities, empowering quicker and more powerful onboarding into creation, lessening costs, and quickening telecom computerized change. By proceeding with OPNFV's tradition of working upstream with cooperation across other open source projects, (for example, LF Networking, LF Edge, CNCF, LF AI, ODIM and other industry associations and norms bodies) and CNTT's administrator center, the undertaking guarantees innovation meets business and operational requirements. Anuket will likewise protect CNTT's nearby working relationship with GSMA, who will keep on distributing the undertaking's Reference Model work.

"It's incredible to see the evolution of what began as the Open Platform for Network Functions Virtualization over six years ago," said Heather Kirksey, vice president of Community and Ecosystem Development at the Linux Foundation. "With Anuket, we are making it easier and more efficient for CSPs to transform their networks and save money, with one, end-to-end platform."

"The Anuket project is unique in covering Operator requirements collection and normalisation, subsequent open-source software development, through to Industry certification programs of ecosystem implementations, all under a single initiative," said Walter Kozlowski, Anuket Technical Steering Committee co-chair and Principal, Cloud Infrastructure Architecture, at Telstra. "I am proud to serve in the role of a co-chair of its Technical Steering Committee, and I am excited by the unique opportunity this project creates for my company and for the whole industry, in the area of network transformation and related telco open infrastructure."

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Gives organisations a competitive edge, Creates a more agile, secure and reliable business, Is the foundation for hyper-converged infrastructures, new application architectures and the cloud.

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Spirent Provides 5G Core Network Testing Axiata Dialog Solution

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CEVA Redefines High Performance AI/ML Processing for Edge AI and Edge Compute Devices

CEVA | January 06, 2022

Consumer Electronics Show – CEVA, Inc.the leading licensor of wireless connectivity and smart sensing technologies and integrated IP solutions, today announced NeuPro-M, its latest generation processor architecture for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor. An industry first, NeuPro-M supports both system-on-chip (SoC) as well as Heterogeneous SoC (HSoC) scalability to achieve up to 1,200 TOPS and offers optional robust secure boot and end-to-end data privacy. NeuPro-M is the latest generation processor architecture from CEVA for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor. NeuPro-M is the latest generation processor architecture from CEVA for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor. NeuPro–M compliant processors initially include the following pre-configured cores: NPM11 – single NeuPro-M engine, up to 20 TOPS at 1.25GHz NPM18 – eight NeuPro-M engines, up to 160 TOPS at 1.25GHz Illustrating its leading-edge performance, a single NPM11 core, when processing a ResNet50 convolutional neural network, achieves a 5X performance increase and 6X memory bandwidth reduction versus its predecessor, which results in exceptional power efficiency of up to 24 TOPS per watt. Built on the success of its' predecessors, NeuPro-M is capable of processing all known neural network architectures, as well as integrated native support for next-generation networks like transformers, 3D convolution, self-attention and all types of recurrent neural networks. NeuPro-M has been optimized to process more than 250 neural networks, more than 450 AI kernels and more than 50 algorithms. 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By distributing control functions to local controllers and implementing local memory resources in a hierarchical manner, the NeuPro-M achieves data flow flexibility that result in more than 90% utilization and protects against data starvation of the different co-processors and accelerators at any given time. The optimal load balancing is obtained by practicing various data flow schemes that are adopted to the specific network, the desired bandwidth, the available memory and the target performance, by the CDNN framework. 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Spotlight

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