millimeter-scale computing - tti/vanguard · millimeter-scale computing . ... 1950 1960 1970 1980...

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University of Michigan 1

Millimeter-Scale Computing

Dennis Sylvester

University of Michigan

Joint work with David Blaauw and many excellent PhD students

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Modern Computing Landscape Cloud Social networking,

Google Docs Mobile Netbooks, tablets,

smart phones Sea of sensors Ubiquitous

computing The next explosion

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The Challenge & Opportunity Tiny nearly invisible computing devices will change

health care, security, infrastructure and environmental monitoring Hey – that’s Smart Dust, right?!

Very hard to achieve Size vs. lifetime

We are close to delivering on this vision What are the killer apps? How small can we make them?

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Retina

Lens

Implant Location

Optic Nerve

AnteriorChamber

High Intraocular Pressure

Iris

Cornea

Ex App: Continuous IOP Monitoring

Glaucoma Second leading cause of blindness; affects 60M Progress checked by measuring intraocular pressure

Continuous monitoring with an implanted microsystem Gives doctors a more complete view of disease Faster response time for tailoring treatments

How many more applications are out there??

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1950 1960 1970 1980 1990 2000 2010 20200.01

0.1

1

10

100

1000

10000

100000

Infla

tion

Adju

sted

Pric

e (1

000s

of U

SD)

Bell’s Law Mainframe

Mini Computer

Personal Computer

Workstation

Smartphone

Laptop

[Bell et al. Computer, 1971, Bell, ACM, 2008]

New class of computing

systems every decade

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Bell’s Law – Corollary Mainframe

Mini Computer

Personal Computer

Workstation

Smartphone

100x smaller every decade [Nakagawa08]

Laptop

1950 1960 1970 1980 1990 2000 2010 2020100m

110

1001k

10k100k

1M10M

100M1G

10G100G

1T10T

Size

(mm

3 )

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University of Michigan 7 1950 1960 1970 1980 1990 2000 2010 2020

100m1

101001k

10k100k

1M10M

100M1G

10G100G

1T10T

Size

(mm

3 )Bell’s Law – Production Volume

Mainframe

Mini Computer

Personal Computer

Workstation

Smartphone


1 per Enterprise

1 per Company

1 per Professional

1 per person 1 per Family

1 per Engineer

Laptop

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University of Michigan 8 1950 1960 1970 1980 1990 2000 2010 2020

100m1

101001k

10k100k

1M10M

100M1G

10G100G

1T10T

Size

(mm

3 )Bell’s Law – Production Volume

Mainframe

Mini Computer

Personal Computer

Workstation

Smartphone


mm-Scale Computing

1 per Enterprise

1 per Company

1 per Professional

1 per person

Ubiquitous

1 per Family

1 per Engineer

Laptop

Ubiquitous

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mm-Computing: Application Areas

Medical Surveillance and micro robotics

mm-Scale Computing

Infrastructure

Textiles

Environment

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1998 2000 2002 2004 2006 2008 2010 20120.1

1

10

100

1000

10000

100000Si

ze (m

m3 )

Where Are We?

[Pister99]

[Sylvester11]

[Blaauw10]

[Pister01]

Mica2Dot

[Park06]

[Roggen06]#

Timeline

[Nakagawa08]

[Hill03]

[Chee08]

Mica2mote

[Hollar00]

Goal - 1mm3 complete sensor

Kris Pister coins “Smart Dust” term

Intel mote#

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Why aren’t we further along? Long device lifetime

vs. small form factor The 3 most important

things in miniaturization Circuits just are not

there yet

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100p1n

10n100n

1µ10µ

100µ1m

10m100m

110

1001k

decade

hour

Powe

r Bud

get,

W

Desired Lifetime, s

minute

weekday

yearmonth

1 mm3 Harvester

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Status of Miniature Sensors Where are we now? 1cc in the research labs 30-50cc commercial motes

Goal: 1mm3 sensor node 1000x improvement Enables host of new applications Tiny wireless sensor nodes that

can be distributed anywhere and everywhere

Challenge: battery size

10,238:1 5hrs of lifetime

1:1 5 years of lifetime

~10,000x

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Battery Trends New battery chemistries

are rare Li-ion ≤7%/year expected

improvement (energy density)

Energy capacity limited by safety and cost

1956 Eveready Alkaline Battery ~72mAh

1992 Rechargeable

Alkaline Battery

2000 mAh

1989 Lithium

Iron Disulfide Battery

3100 mAh

1896 Columbia Dry Cell

Zinc Carbon Battery

2005 LSD NiMH

Battery 2300 mAh

1961 Nickel

CadmiumBattery

1100 mAh

1960 Zinc

Carbon Battery

1100 mAh

[Broussely04]

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Energy Harvesting

µW/cm3

Photovoltaic (outside)

15,000#

Air flow 380

Vibration 200

Temperature 40#

Pressure Var. 17

Photovoltaic (inside)

10#

[Courtesy: Jan Rabaey, S. Roundy]

Microturbine

Air Flow

Solar

Capacitive Vibration

Harvesting

Capacitive Vibration

Harvesting

Temperature Gradient

Piezoelectric

# fundamental metric is µW/cm2

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Harvesting Improvements Limited Energy harvester efficiency gains are modest Fundamentally limited by harvesting source

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Past Michigan Sensor Designs

Phoenix 2 Design (2010) - 0.18 µm CMOS

- Commercial ARM M3 Core - Solar harvesting / PMU

-28 µW/MHz

Subliminal 2 Design (2007) - 0.13 µm CMOS

- Study variability effects - 3.5 µW/MHz

Subliminal 1 Design (2006) -0.13 µm CMOS -investigate Vmin

-2.60 µW/MHz

Phoenix 1 Design (2008) - 0.18 µm CMOS

- Minimize sleep current - 2.8 µW/MHz / 30pW sleep power

processor

memory

244µm

305µm

122µm

181µm

processor

memory

244µm

305µm

122µm

181µm

IOPM (2011) - 0.18 µm CMOS

- MEMS/CDC - Solar / PMU

- Wireless comm

EE Times 20 Hot Technologies for 2012

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Microsystem functions include sensing, processing, storage, and transmission All components must be re-examined to fit within power

envelope defined by power sources and power management

mm3: How Do We Get There

Sensors and Front End

Microprocessor Memory

Wireless Communication

Power Management

Wakeup Controller

Antenna

Power Sources

MEMS Sensors

Wakeup Controller /

Timers

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How? Best reported energy

efficiencies for: Microprocessors/microcontrollers Timers Static memories CMOS image sensors Voltage references Signal processing cores

Near-threshold computing is the key Voltage scaling in CMOS circuits

must be re-established

Tradeoffs abound Speed, area, jitter, SNR, etc. We seek 10X reductions in power/

energy with reasonable tradeoffs

Vopt

Ene

rgy

/ Ope

ratio

n Lo

g (D

elay

)

Supply Voltage 0 Vth Vnom

~3 -10X

~2X

~5-10X

Vbal Vmax

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Microsystem functions include sensing, processing, storage, and transmission All components must be re-examined to fit within power

envelope defined by power sources and power management

mm3: How Do We Get There

Sensors and Front End

Microprocessor Memory


Power Management

Wakeup Controller

Antenna

Power Sources

MEMS Sensors

Wakeup Controller /

Timers

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100p

1n

10n

100n

1µ

10µ

100µ

1m

Powe

r Con

sum

ptio

n (W

)

Time (min)0 20 40 60

100 ms

1 ms

Ex: Why Timers Are So Important

Power consumptions in various modes of ULP sensor

1

Base Station

Sensor Node

IdleSensor measure

TX / RX

Asymmetric RF communication does not require precise timing

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100p

1n

10n

100n

1µ

10µ

100µ

1m

Powe

r Con

sum

ptio

n (W

)

Time (min)0 20 40 60

100 ms

1 ms

Why Timers Are So Important


1

Base Station

Sensor Node

IdleSensor measure

TX / RX

Meas. 10%

TX/RX 11%

Idle 79%

Energy 0.91 µJ/hr

Asymmetric RF communication does not require precise timing

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100p1n

10n100n

1µ10µ

100µ1m

Powe

r Con

sum

ptio

n (W

)

Time (min)

100p1n

10n100n

1µ10µ

100µ1m

0 20 40 60

0 20 40 60

IdleSensor Measure Synch. Mismatch

TX / RX

Timer



1

Sensor Node

Sensor Node

Asymmetric RF communication does not require precise timing Symmetric RF communication requires precise timing Energy penalty for mismatch can dominate energy budget

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100p1n

10n100n

1µ10µ

100µ1m

Powe

r Con

sum

ptio

n (W

)

Time (min)

100p1n

10n100n

1µ10µ

100µ1m

0 20 40 60

20 40 60

Mismatch (1s)

0


TX / RX

Timer



1

Sensor Node

Sensor Node


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100p1n

10n100n

1µ10µ

100µ1m

Powe

r Con

sum

ptio

n (W

)

Time (min)

100p1n

10n100n

1µ10µ

100µ1m

0 20 40 60

20 40 60

Mismatch (1s)

0


TX / RX

Timer



Synch. Mismatch

97%

Energy 103 µJ / hr

1

Sensor Node

Sensor Node


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Keeping Time with Picowatts Crystal oscillators bulky and

power hungry RC oscillators preferable,

exhibit accuracy vs. power tradeoff

Low power commercial crystal oscillator ~400nW, 5x3mm

[Micro Crystal Switzerland RV-2123-C2]

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Keeping Time with Picowatts Crystal oscillators bulky and

power hungry RC oscillators preferable,

exhibit accuracy vs. power tradeoff

Vin Vs

Vclk

ML1

MS1

MS2

MS3

MS4

MS5

MS6MC1

INV1 INV2

TINV

Vinv

Vin Vs Vout

MC2

vx

MI1

MI2

MI3

MI4

Gate leakage current based

timer 1pW!

Low power commercial crystal oscillator ~400nW, 5x3mm

[Micro Crystal Switzerland RV-2123-C2]

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Putting it together: 1.5mm3 microsystem Continuous intraocular pressure monitoring Wireless communication Energy-autonomy Device components

• Solar cell • Wireless transceiver • Cap to digital converter • Processor and memory • Power delivery • Thin-film Li battery • MEMS capacitive sensor • Biocompatible housing

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Active Mode PowerCDC 7.0 µWTransceiver 47.0 mW

• µP @ 100 kHz 90.0 nW

Energy/Day

6.3 µJ

1.7 µJSCVR 116.9 nW 2.2 µJ

Standby Mode PowerCDC 172.8 pWTransceiver 3.3 nW

• WUC 62.0 pW

SCVR 174.8 pW• 4kb SRAM 9.8 pW

Energy/Day14.9 µJ

846.7 nJ15.1 µJ

5.2 µJ

Time/Day19.2 sec

134.4 µsec

19.2 sec19.2 sec

Time/Day24 hours24 hours

24 hours24 hours

24 hours

134.8 µJ

285.1 µJ

IOP Monitor Power Budget Measure IOP every 15 minutes DSP with 10k processor cycles @ 100 kHz per measurement Daily wireless transmission of 1344b raw IOP data

5.3 nW average power 1 month lifetime with no harvesting

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Other Biosensor Applications Pressure is an early biomarker

for tumor health Implant sensor during biopsy 1mm size makes delivery

through same needle feasible

Track pressure to determine response of tumor to chemotherapy Adjust medication after 1 – 2

weeks if necessary Earlier indicator than size of

tumor (6 – 9 weeks)

Also: Smart orthodontics, intracranial pressure, others

Implanted Biosensors

(pressure, pH)

Wireless data transfer

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Power from the Bottom Miniature Benthic Microbial

Fuel Cells Generate electricity from

microbes in marine sediment – anaerobic conditions Low harvest: 14uW/cm2

Traditionally requires large deployments – at high cost mm-sensor node

enables a small “dart” – cheap and fast deployment

UUVs, etc Work with SPAWAR

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Status: M3 Michigan Micro Mote

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1mm3 Platform

Battery and Solar Harvesting

Processor

Memory

Timer

MotionDetect

Imager


ImagingSolar

Harvesting

Processingand Wireless

New Applications

Biology

Pika

Medicine

ICP

Security

Conclusions Applications of mm-scale computing are endless and often

unimaginable today But first the hardware must get there (which it is)

Power minimization is paramount Few nW avg power 1m comm range ~Indefinite lifetime Re-think entire sensor

system from bottom up

Chip in cells?

millimeter-scale computing - tti/vanguard · millimeter-scale computing . ... 1950 1960 1970 1980...

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