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The Race To Neural Class Networks Tina Tsou Workshop on “Machine Learning for 5G and beyond”

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Page 1: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

The Race To Neural Class

Networks

Tina Tsou

Workshop on “Machine Learning for 5G and beyond”

Page 2: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Building IT that works like the brain

“All significant mental algorithms are learnedexcept for the learning and reward machinery itself”

Page 3: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Artificial Intelligence

Perform tasks that normally require human intelligence

Machine Learning

Replace instructions with a neural network to parse data, learn, and make predictions.

AI, Machine Learning & Deep Learning

3

Deep Learning

Increase the layers and run massive amounts of data through the system to train it.

Page 4: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

1 Billion Parameters 1 Billion Users

“Neural-Class” Networks

Deep Neural Network Hyperscale Environment

Page 5: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Year 2000 Server CPU Networking

CPUs 1x Pentium Cores 1

100 Servers x 1 Core = 100 Cores

Page 6: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Year 2018 Server CPU Networking

CPUs 1x Qualcomm Centriq 2400Cores 48

100 Servers x 48 Cores = 4,800 Cores

Page 7: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Year 2018 Server GPU Networking100 Servers x 46,080 Cores = 4,680,000 Cores

Page 8: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

The Future of Networking

CPUs

GPUs

LANAccelerators

Page 9: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

A HUGE New Class of Data Centers

6/16/2019 9

Millions

Billions

ThousandsData Centers

Micro Data Centers(Edge Computing)

Devices

Page 10: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

6/16/2019 10

Page 11: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Use Cases

6/16/2019 11

Complex

Edge Gateway

Micro Data Center Rack

Page 12: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Server Configured Like a Human Brain

1.3 Billion Terabytes of Storage

10,000 Network Ports 100,000 Miles of Cabling Included

>100 PetaFLOPS Processing Power

20W Power

77 Cubic Inches19” Wide x 1.75” (1U) High x 2.3” Deep

3.3 Pounds

12

Page 13: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

© 2

01

8A

rm L

imit

ed

SS

S S

S

C

Compute

CA

CCA

CCA

CCA

Acceleration

Arm is uniquely positioned in “device-to-cloud”

Storage

Edge computing – necessary to handle the massive upstream data

Local Services(Gateways)

IoT Device (Data Source)

Public / Private Cloud

Infrastructure Services

Edge cloud

13

Page 14: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

14 © 2019 Arm Limited

Compute solutions Device-to-Cloud

Corstone secure foundationsfor Cortex processors

Neoverse platformsML platforms

Page 15: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

15 © 2019 Arm Limited

Arm’s Heterogeneous ML Platform – Hosted & Embedded

Dynamic (Hosted) Compilers & Drivers

TFLiteµ

CodeGen (LLVM)

Embedded (Offline) Compilers

Applications

Higher level frameworks(e.g. TensorFlow, Caffe, Android NNAPI)& third-party tools

Op

timizatio

n &

Pro

filing To

ols

Runs on A-Class Host CMSIS-NN

CPU GPU Hosted NPU CPUNPU MCUEmbedded

NPU

ACL

Android or Linux

RTOS

Application Processors Embedded Deeply Embedded

Page 16: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Akraino Release 1

16

Page 17: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Akraino R1: Unifying the Edge

17

Akraino Edge Stack Issues Premier Release, Sets Framework to Enable 5G, IoT Edge

Application Ecosystem

● Inaugural release unifies multiple sectors of the edge across disciplines, including IoT, Enterprise,

Telecom, and Cloud

● Delivers tested and validated deployment-ready blueprints

● Creates framework for defining and standardizing APIs across stacks, via upstream/downstream

collaboration

SAN FRANCISCO – June 6, 2019 – LF Edge, an umbrella organization within the Linux Foundation that aims to

establish an open, interoperable framework for edge computing independent of hardware, silicon, cloud, or

operating system, today announced the availability of Akraino Edge Stack Release 1 (“Akraino R1”). Created via

broad community collaboration, Akraino’s premiere release unlocks the power of intelligent edge with deployable,

self-certified blueprints for a diverse set of edge use cases.

NEWS

http://www.lfedge.org/
http://www.linuxfoundation.org/
https://www.lfedge.org/projects/akraino/
Page 18: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Role of Akraino Blueprints in Open Source Lifecycle

Successful Open Source Development depends on the complete life cycle of projects, products that market will adopt and deploy

PROJECTS

PROFITS PRODUCTS

DEVELOPER

COMMUNITY• Akraino’s blueprint model enables rapid transition from projects to PoC/deployment, cutting down integration time significantly.

• Blueprints augment Open Source Projects to address & accelerate Interoperability, Packaging, and Testing under Open and neutral governance.

Page 19: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Akraino R1: Tested & Validated Blueprints

19

Blueprints - approved & tested declarative configuration

based on use cases, set of hardware, POD & software

Reference Architecture - defines Akraino building blocks

Declarative Configuration - hides lower layer complexity

to user

CI/CD, Integration & Testing Tools - drive product quality

• 11+ Blueprint families, 20 Blueprints under

development

• Community-tested & validated on real

hardware, Akraino Labs by members and

community.

Page 20: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Tested & Validated Deployment-Ready Blueprints Details

20

Page 21: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Akraino R1 Blueprints Details

21

Blueprint Family Blueprint Primary Use Case Industry Target Blueprint Summary

Network Cloud (NC)

Unicycle with SR-IOV Telco Edge use cases (Multi Server). Multiple

applications

Telco, Enterprise NC blueprint family enables hardware configuration

and automated deployment of telco grade multiple

edge sites from a remote regional controller. Rover Telco Edge use cases (Single Server). Multiple

applications

Telco, Enterprise

Unicycle with OVS-DPDK Telco Edge use cases (Multi Server). Multiple

applications

Telco, Enterprise

Telco Appliance Radio Edge Cloud (REC) Appliance for Radio Access Network (RAN), RAN

Intelligent Controller and Near realtime Edge

MEC Appliance

Telco 5G, Enterprise Appliance tuned to support the O-RAN Alliance and O-

RAN Software Community's Radio Access Network

Intelligent Controller (RIC)

Integrated Edge

Cloud (IEC)

Type 1 (small Edge) Telco or enterprise application deployment on

Arm servers

Telco, IOT and Enterprise IEC enables the new functionalities and deployment

model on the network edge. It supports ARM

processors and architecture.

Type 2 (Medium Edge) Telco or enterprise application deployment on

Arm servers

Telco, IOT and Enterprise

StarlingX Far Edge Distributed Cloud Enterprise edge and Far edge. Multiple

applications

Enterprise & IOT Addresses edge and Far edge use cases at high

density locations such as malls, airports and sports

stadiums to support value added services at these

events and locations.

Kubernetes- Native

Infrastructure for

Edge

Provider Access Edge vRAN and MEC (AR/VR, Machine learning, etc., ) Enterprise & Telco Blueprints in the Kubernetes-Native Infrastructure for

Edge family leverage the best-practices and tools from

the Kubernetes community to declaratively and

consistently manage edge computing stacks from the

infrastructure up to the workloads.

Edge Lightweight

and IOT blueprint

(ELIOT)

IOT Gateway IOT IOT & Enterprise ELIOT targets on making the edge node a lightweight

software stack which can be deployed on limited

hardware capacity.uCPE uCPE Enterprise & Telco

https://www.o-ran.org/
https://o-ran-sc.org/
https://gerrit.o-ran-sc.org/r/admin/repos
Page 22: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Akraino R1 Blueprint: Radio Edge Cloud

22

Radio Edge Cloud for 5G use cases

Target Industry: Telco 5G, Enterprise

Purpose/Features

• Telco- grade edge cloud platform for near-real time container workloads.

• Integrated with open-source RAN Intelligent Controller (RIC) – helps deploy customized, tailored apps with Zero touch

• Automated CD pipeline testing the full software stack

* Target architecture shown here and the fully installable building blocks is in R1

Page 23: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Akraino R1 Blueprint: IOT & Remote Edge

23

Integrated Edge Cloud

Target Industry: Telco, IoT, Enterprise

Purpose/Features

• Addresses IOT use cases

• Targets telco edge applications & medium edge cloud deployments with Arm, based on Kubernetes and Calico

• Automated installation, integrated with SDN-Enabled Broadband Access (SEBA) use case

Type 1 Type 2

Page 24: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Functional View: R1 Blueprints in Akraino Edge Stack

24

Application Interoperability

LF Edge

InfrastructureDevice Edge Control Edge Gateways (Thin and Thick) Industrial/ Telco

Radio Edge Appliances /HCI

MDC Edge

On-Prem DC Edge

Telco/Cloud Edge

Blueprints

24

ELIOT IOT GW/uCPE

Radio Edge Cloud (REC)

Network Cloud (Unicycle, Rover)

IEC (Type 1 & 2)

Far EdgeStarlingX

Provider Access Edge (PAE)

Page 25: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

Akraino R1: Key Takeaways & What’s Next in 2019

1. LF Edge Projects gaining community

support with Akraino aimed at

accelerating time to deployment ->

Projects to Products & Production

1. Akraino establishes unified framework

for Edge collaboration & validation

across projects & community with

Blueprints

1. Akraino’s R1 releases 10+ Blueprints for

IOT, Enterprise and Telco Edge Cloud

25

On the Horizon

› New blueprints (Gaming, Connected Cars…) +

enhancements to existing blueprints

› Tools for automated blueprint validations

› Edge API’s in collaboration with LF Edge projects

› New community lab hardware

Page 26: The Race to Neural-Class Networks - ITU · Machine Learning Replace instructions with a neural network to parse data, learn, and make predictions. AI, Machine Learning & Deep Learning

The Race to Neural-Class

Networks

Tina Tsou

Workshop on “Machine Learning for 5G and beyond”


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