Designing for one trillion devices - Cambridge Wireless · 2019-07-05 · •Average volume needed...

Paul WilliamsonJuly 2019

Designing for one trillion devices

@Arm @pnwilliamson

2 © 2019 Arm Limited2 © 2019 Arm Limited

A trillion things must work separately, together, automatically and resiliently

Agenda

Technology gaps

A trillion things

The road forward


• Are there a trillion things?

• Yes, if you include consumables

A Trillion is a Lot

Scale and growth dominate

Cu

mu

lati

ve

62 Billion

2017 2022 2027 2032

1 Trillion

126 Billion

4 © 2019 Arm Limited

Today’s trillions are not electronic


Industry

Commercial facilities,warehouses

Publicinfrastructure

Management& maintenance

Automotive

Logistics & optimization

Home

Smart meters,monitors

Agriculture

Water quality, humiture, soil

Disaster management

Flooding, earthquakes,landslides

What Will the Trillions Do?


<#>

The Basic IoT Algorithm

• Gather a lot of data

• Perform some

processing

• Send results to

cloud app

For each sensor

• Gather data from

lots of sensors

• Process and look for

useful information

• Do something with that

information

For central app

• Implement instructions

from cloud app

Optional actuator



World RF designers: ~5,000• Average volume needed for trillion radios: 200,000,000

World IC design teams: ~20,000• Average volume needed for trillion objects: 50,000,000

People who can write code : ~20,000,000• Average volume needed for trillion objects: 50,000

How do you design a trillion chips?



Consistent accessible platforms look essential


<#>

How Do You Build a Trillion Chips?

About 30% of total annual worldwide production

TSMC annual capacity (2015) 9,000,000 wafers

2mm2 per sensor chip, 35000 per 300mm wafer

28,000,000 wafers



240 mAh coin cell (CR2032)

Weighs 3g, contains about 109mg of Lithium

1 trillion cells contain 109 billion grams, which is 109,000 Metric tons

How Do You Build a Trillion Batteries?

32,500 Metric tonsApproximate annual worldwide Lithium production



Energy Harvesting looks like a good idea


EH

Source

InputPower

Conversion

OutputPower

Conversion

StoredEnergy

Time

MCU

Store StoreStoreBootRun

Run Run Die!

Battery-less Operation

1ms

Losses


<#>

Moving the Goalposts

Both leaders using Arm Cortex-M

Scored 377 at launch against 187 of nearest competitor

Sub-threshold operation enabled Ambiq micro to set a new record in ULP benchmarks



Voltage from

solar cell

T

0.6

0.3

1.2

Use it or

lose it

Charging Direct operation Discharging

Store energy

in battery

84%

16%

Solar

Energy

0.3 ≤ VMPP < 0.6

VMPP > 0.6

Energy harvesters have variable output, causing waste of available energy

Significant gains possible from direct-operation under some conditions

Scheduling work for these periods is an interesting challenge

63%

37%Compute

Cycles

Direct operation

sub-threshold

Near-threshold

from battery

System level optimisation: Use it or lose it

A. Savanth, Integrated Reciprocal Conversion with Selective Direct Operation for Energy Harvesting Systems, TCAS 2017


Demonstration 1uW compute


How to make the IoT work safely and securely

Needs to workseparately

01 03 0402


Energy

Systems challenge from transistors to applications

Needs to work together across ecosystem

Process technology

Power efficient RTL

Application SW layer

Power management policies/drivers

Low power libraries

SoC architecture

System components – memory/display/PMU

IoT Constraint


Optimize across the system to achieve lowest power

• Run a Cortex®-M0 for 10 cycles

• Write one bit of flash

• Write ~300 bits of DRAM or SRAM

• Send ~5 bits across LPDDR4

• Transmit 2 bits of UWB data

• Transmit 0.02 bits over Bluetooth LE

• Drive an electric car 100fm (@1MJ/km)

~0.05% of the distance across Si atom

Energy Efficiency: Things You Can Do With 100pJ



01 03 0402

Needs to worktogether


Martin Michael LomasneyPolitical Boss, Boston’s 8th Ward

Never write if you can speak; never speak if you can nod; never nod if you can wink”

“


Massive data, low information content

Video Monitoring



01 03 0402

Needs to workautomatically


Has a solution that can be graded objectively

Lots of data available for training

Is difficult to describe the solution in a program

Would take too much effort to do manually

What Makes a Good Machine Learning Problem?



01 03 0402

Needs to workresiliently


Gerald WeinbergAuthor of The Psychology of Computer Programming

If builders built buildings the way programmers wrote programs, then the first woodpecker that came along would destroy civilization.”

“


Platform Security Architecture

Analyze

Implement

Certify

Architect

Hardware & firmware architect specs

Firmware source code

Independently tested

Threat modellingCommon principles across

multiple use cases

Device identity

Trusted boot sequence

Certificate-based

authenticationSecure over-the-air

software update

A framework for building secure devices – openly published


Creating an IoT Security Ecosystem

Developed by Certified partners

psacertified.org


Needs to workautomatically

Needs to workresiliently

Needs to worktogether

Needs to workseparately

Key points for IoT

03 040201

Designing for one trillion devices - Cambridge Wireless · 2019-07-05 · •Average volume needed...

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