IoT – Life at the Edge

Nick Hunn – WiFore Consulting

The IoT story so far…

Towards 50 Billion Connected Devices

Amount of time spent discussing

LPWAN

Amount of time spent discussing the rest of

the IoT

Amount of time spent designing products

Amount of time spent discussing AI

The Comms part is largely done

LPWAN exists

• Sigfox

• LoRa

• Telensa

• Ingenu

• NB-IoT

Low cost data exists

• Sigfox

• LoRa

• 1NCE

Just because it will get better is not a reason for prevarication.

IoT Basics

Data Capture Data Insight

There’s a lot of detail in between…

The IoT value stack

Deployment & Physical installation

Algorithm Development

Additional Data Sourcing

Business Applications (vertical)

Business Applications (packaged)

IoT Analytics

Cloud

Device Management

Data Contracts

Comms

Project Management

Data Cleansing & Verification

Security & Updates

Provisioning

Sensor & Physical

Deployment

Applications& Analytics

M2M / IoT Infrastructure

(DLC – DeviceLife Cycle)

Connectivity

HardwareEDGE

The world is producing excessive amounts of “unstructured data” that need to be reconstructed.

Rob High – CTO, IBM

Big Data doesn’t need to reside in one place.

Lots of Little Data is also Big Data.

Learning can be distributed.

Because Intel wants to sell more server chips.

Because CISCO wants to sell more infrastructure.

Because the network operators need a story to support 5G.

Why is edge computing such a well kept secret?

And also because it’s difficult.

The balance of power

Cloud

• Limited processing power• Limited resources• Limited battery life• Intermittent connectivity

• Lots of processing power• Lots of resources• Mains powered• Aggregated Data• Additional Data Sources

Processing Power

Thing

The balance of power

Thing Cloud

• May need to make real time decisions• Can’t guarantee a connection

• May have limited data throughput• Intermittent uploads • Very limited downloads• Little access to additional data

• Difficult to make real-time control decisions for millions of devices

Autonomy

The processing hierarchy

Cloud• Heavy Lifting• “Unlimited” resources

Mobile• Pre-programmed and

learned models• Video processing, etc.

ThingEdge• Real-time learning• Autonomous operation

Giga (Billion) Operations per second and Trillion Operations per second

TOPS and GOPS

Intel Xeon 8180M 0.3 TOPS / WNVIDIA 0.4 TOPS / W

Thing

< 0.05 TOPS 2 - 3 TOPS 25 - 50 TOPS

GreenWaves 0.6 TOPS / WKneron offers 1.5 TOPS / WARM ML 3 TOPS / WNovumind 3 TOPS / W

Cambricon 3 TOPS / WMythic 4 TOPS / WGroq 8 TOPS / WSyntiant 20 TOPS / W

Is it training or is it inference?

MLP - Multi-layer Perceptron

CNN - Convolutional Neural Networks

RNN - Recurrent Neural Networks

DNN - Deep Neural Networks – image recognition & voice

The AI Landscape

Machine Learning

Neural Networks

Deep Learning

Video Neural Network Engines and AI accelerators

Sunrise AI chip for Facial Recognition

Supports 4 x 1920 x 1080 30fps video inputs at under 1.5W

Horizon Robotics

Automotive and Audio

Google’s Edge TPU

“Edge-based ML inference is vital to delivering reliable, live, low-latency, and cost-effective smart city IoT. Cloud IoT Edge and Edge TPU unlock these capabilities in new ways for the next generation of Smart Parking systems.”

John Heard, Chief Technology Officer, Smart Parking Limited

Edge TPU Features“The first step in a roadmap that will leverage Google's AI expertise to follow and reflect in hardware the rapid evolution of AI.”

• Inference Accelerator• Dev boards coming soon

The IoT is getting smarter…

Are you?

Nick HunnCTO

mob: +44 7768 890 148

email: nick@wifore.com

web: www.wifore.com

Creative Connectivity Blog: www.nickhunn.com

LinkedIn: www.linkedin.com/in/nickhunn

Questions?