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FromMarconi

to

Moore

Circuitsand

Systems

for

Communications

–

StillaChallenge?

Acknowledgements: Prof. H. Meyr, M. Witte,

F. Borlenghi (RWTH-Aachen)



Guglielmo Marconi

1897 : Wireless Telegraph Company

1909 : Nobel Price in Physics

2

„It isdangerousto put limitsonwireless“

Source: Intel Corporation

GordonMoore

1968:CofoundedINTELCorporation

2005

:

Marconi

Society

Lifetime

AchievementAwardMoore‘slaw (1965/75) pacestheevolution

of integratedcircuitsuntiltoday



Marconi’sfirst wirelesstelegraph(1895)

3

Mechanicalcontinuouswave RF signalgenerator

Core of an early daysradio telegraph receiver

Electrochemical demodulatorfor voice ~1900



4

De Forest Audion radioreceiver from 1906

Texas Instruments firstsilicon transistor (1954)

First transistor radio:TI Regency TR-1 (1954)



5

Mobilesubscriptions

outrunfixedtelephone

subscriptions

Numberof fixedline

subscriptionsstarts

decreasing

Broadbandmobilesubscriptions

outrunfixedinternetsubscriptions

M.Witte,2010



Introduction:SomeHistory

ScalingLaws,Trends,andObservations

Arethere

still

challenges??

• SomeexampleswhyIthinkYES

TheEndof Moore’sLaw

• Thelimitof wirelessORmotivationforsomemorefancyresearch

6



7

Moore’sLaw

2xevery

24months

2 x e v e r y

1 8 m o n t h s

E d h o l m

’ s L a w



Newmultiplexing

schemes

allow

to

allocate

more

bandwidthtoasingleuserforhigherpeakthroughput

Spectralefficiencyincreasesdueto

• Higherordermodulationschemes

• Spatialmultiplexing

8

GSM

•270kHz

•GMSK

EDGE

•270kHz

•8PSK

EEDGE

•2x270kHz

•32QAM

UMTS

•5MHz

•QPSK

HSPA

•5MHz

•16

QAM

HSPA+

•2x2MIMO

•64QAM

LTE

•4x4MIMO

•20MHz

LTEA

•8x8MIMO

•20100MHz

802.11

•DBPSK

•11MHz

802.11b

•CCK

•11MHz

802.11a/g

•64QAM

•20MHz

802.11n

•40MHz

•64QAM

•2x2MIMO

802.11n

•40MHz

•64QAM

•4x4MIMO

802.11ac

•80160MHz

•256QAM

•8x8MIMO

802.11

ad•1.7GHz

•16QAM

Morecomplex

receivers



Imbalancebetween

complexity

and

integration

density

Dataratedoublesevery18months

Algorithm

complexity

grows

(spectral

efficiency)

9

2x every24 months

2x every18 months

«Complexityof

baseband

processing

outruns

technologycapabilities»



10

Moore´slaw:

2xevery

24

month

Basebandcomplexity

2xevery35month

C o m p l e x i t y [ G

a t e e q u i v a l e n

t s ]

Datacollectedby

M.Witte,2010

Some empirical data:evolution of baseband

complexity over time

Year of publication1994 2010

1M

10M

100M

100k



11

Moore´slaw:

2xevery

24

month

Basebandcomplexity

2xevery35month

C o m p l e x i t y [ G

a t e e q u i v a l e n

t s ]

Datacollectedby

M.Witte,2010

Some empirical data:evolution of baseband

complexity over time

Year of publication1994 2010

1M

10M

100M

100k

Numberof Transistorsrequiredfor

integrationgrows

less

rapidly

than

“complexity”



Technologyscaling

reduces

both

area

and

delay

Example

12

180 45

Feature size [nm]

# G a t e s / m

m 2

O p . f r e q u e

n c y

180 45

Feature size [nm]

180 45

Feature size [nm]

G o p s / s / m m

2 Some saturation

around 65-45nm

180nm 90nm 45nm

Clock freq. 100MHz 200MHz 400MHz

16x16 Mult+

overhead(50/50)20kum2 5kum2 1.25kum2

5Gops/s/mm2 40Gops/s/mm2 320Gops/s/mm2



13

2000

• Liuetal.

• 112mm2

• 250nm

2004

• Uvieghara etal.

• 46mm2

• 130nm

2006

• Luftner etal.

• 43mm2

• 90nm

2009

• Shirasaki etal.

• 66mm2

• 45nm

2G 2.5G 3.5G3G

Someexamples

of

digital

cellular

ASICs

from

ISSCC



Cellularmodems

require

multi

standard support

Neverthelessthenumberof discretemodemcomponents

decreasesrapidly

14

0

5

10

15

94 97 00 03 06

C o m p o n e n t

s

Source: Dr. H. Eul,Keynote at 2010VLSI Conference

•Reduces cost (PCB, packaging, andmanufacturing

•More space for battery and display

GSMEDGEWCDMAHSPA+LTELTE-A

3G (WCDMA/HSPA)

2G (GSM/EDGE)

Legacy supportguarantees

coverage



Integrationof

application

and

modem

functionality

• Additionalairinterfaces,connectivityoptions,andstorage

• 3DGraphicsandVideo

• Powerfulapplicationprocessors

15

C o m m u n i c a t i o n

A p p l i c a t i o n

Ito et al.; ISSCC 2007

3GPPModemcovers

40%of thechiparea



Powerconsumption

and

energy

efficiency

16

Energy efficiency

Data download

Power consumption

Standby/voice

Determinedbyleakage

andstandbyactivity

Determinedbyactive

powerconsumption



Powerconsumption

and

energy

efficiency

17

Energy efficiency

Data download

Power consumption

Standby/voice

Determinedbyleakage

andstandbyactivity

Determinedbyactive

powerconsumption

J. Ayers, et al..,”An Ultralow-Power Receiver for Wireless Sensor Networks,” JSSC 2010

P. Petrus, et al., ” An Integrated Draft 802.11n CompliantMIMO Baseband and MAC Processor,“ ISSCC 2007

Simple OOK radio for sensor nodes

0.18 nJ/bit (complete transceiver)

Technology: 0.18 um Technology: 0.18 um

802.11n WLAN transceiver

3 nJ/bit (digital PHY/MAC only)

Highspectralefficiencycomesatthecostof poorenergy

efficiency



18

OFF Standby Voice Data

Useful datatraffic

Power

Verypoor Poor OK Good

Energy efficiency



19

OFF Standby Voice Data

Useful datatraffic

Power

Verypoor Poor OK Good

Energy efficiency

Leakageandstandbycurrentsdominateas

• DSPbecomesmoreenergyefficient

• Workloaddecreases(e.g.,standby)

Challenge:EnergyProportionality

Usehighenergywhen

needto“workhard”Lowenergywhen“doing

little”is“goodenough”



S. Kunie, et al., ” Low power architecture and designtechniques for mobile handset LSI Medity M2,“ ASP-DAC, 2008

176 9550 250

500

0

200

400

600

800

Tx Rx

RF BB PA

45nm >40%of thedigitaldiecovered

bybaseband

signal

processing

RX:Basebandconsumesmostof thetotalpower

285 360345

540300

0

500

1000

Tx Rx

RF BB PAS. G. Sankaran, et al., ” Design and Implementationof a CMOS 802.11n SoC,“ Comm. Magazine 2009

>70%areacoveredby

basebandsignal

processing

DSPconsumessignificantpowercomparedtoRF(especiallyRX)

130nm

Letscheck

two

examples

2x2MIMOWLAN(IEEE802.11n)

3GPPHandsetASIC/MPSoC

20



21



22

MIMO:Transmit

multiple

data

streams

concurrentlyinsamefrequencyband

Usedinalmostallimportantstandards

Taskof theMIMOdetector:

Separationof multiplexeddatastreams

Choiceof theMIMOdetectorhassignificant

impactonperformance

OptimumMIMOdetection:

Straightforwardsolution:Checkallcandidates

Numberof candidates:exponentialin

spectralefficiency



2002:4stream

MIMO

over

UMTS

Spectralefficiency8bits/s/Hz

Examine256candidates

4million

times

per

second

23

Source: Bell Labs Wireless Research, Holmdel, NJ

2002 : MIMO over UMTS with1 Mbps for 31 users (8 bits/s/Hz)

2009:MIMOWLAN

Spectralefficiency24bits/s/Hz

Examine

2

24

candidates

40milliontimespersecond

2009 : MIMO WLAN 600 Mbps(24 bits/s/Hz)



Spheredecoding

Maptheproblemtoatreesearch

Usebranchandboundstrategy

forcomplexity

reduction

STSspheredecodingprovides

softinformationforchanneldecoder

24

2007 : STS Soft-outputsphere-decodingwith 10-40 Mbit/s

250nm2mm2

71M nodes/s

Requires

completelynew

architectures

Treesearchisverydifferent

fromtypical

DSP

algorithms



Spheredecoding

Maptheproblemtoatreesearch

Usebranchandboundstrategy

forcomplexity

reduction

STSspheredecodingprovides

softinformationforchanneldecoder

25

2007 : STS Soft-outputsphere-decodingwith 10-40 Mbit/s

250nm2mm2

71M nodes/s

Requires

completelynew

architectures

Treesearchisverydifferent

fromtypical

DSP

algorithms

2mm

2mm

25mm2

Nearoptimumperformance@600Mbps

4parallelinstances

workatat320MHz

1.28Gnodes/s

802.11n

Technology shrink &architecture optimization



Exchangereliability

information

between

MIMO

detector

andchanneldecoder

Convergetooptimumsolutioninmultipleiterations

Iterationsrequiresoftin

softoutMIMOdetection,

whichis

even

more

complex

ComplexityforN iterations

increasesatleastNfold

26



3GPP2007:

Extension

of

2G

system

GSM

/

EDGEtowardhigherdatarates

Highermodulationorder(16QAMand32QAM)

1.2xhighersymbolrate

Bandwidthremains

unaltered

Optimumreceiver:Maximumlikelihood

sequenceestimation(MLSE)

Complexitygrowsexponentiallyin

spectral

efficiency

and

channel

length

27

modulation order / alphabet size

branches

2 4 8 16 324

64

1024

65k

1000k

GSM

EDGE

EvolvedEDGE

Strongneed for equalization

Impractical evenina32nm process

32QAM Tx-signal(Evolved EDGE)



Solution:channel

shortening

with

decision

feedback

sequence

estimation

Ordersof magnitude

complexityreduction

28

channelestimation

inputbuffer

coefficientscomputation

FIR filter

pre-filtering channel equalizer channel decoder

Viterbidecoder

turbodecoder

decoder

inputmemory

DFSE withadaptive numberof trellis states:

8 (GMSK)8 (8PSK)

16 (16QAM)32 (32QAM)

sharedmemory

130nm

Complexity exceedsDSP capabilitiesevenwithadvanced processnodes



Solution:channel

shortening

with

decision

feedback

sequence

estimation

Ordersof magnitude

complexityreduction

29

channelestimation

inputbuffer

coefficientscomputation

FIR filter

pre-filtering channel equalizer channel decoder

Viterbidecoder

turbodecoder

decoder

inputmemory

DFSE withadaptive numberof trellis states:

8 (GMSK)8 (8PSK)

16 (16QAM)32 (32QAM)

sharedmemory

130nm

Complexity exceedsDSP capabilitiesevenwithadvanced processnodes

Averg. power at VDD=1.2V

EDGE (8PSK, CC) 6.8mWE-EDGE (16QAM, TC) 11.2mW

E-EDGE (32QAM, TC) 19.9mW

Dedicated ASIC solution[Benkeser et al., ISSCC2010]



Example:Low

Density

Parity

Check

Decoder

1962:inventedbyR.G.Gallager

• PerformanceclosetotheShannonlimit(onparwithTurbocodes)

• Initiallyconsideredtocomplexforeconomicimplementation

1999:

re

discovered

by

MacKay

and

Neal• VLSItechnologyallowedfortheimplementationof LDPCcodes

Today:LDPC

codes

are

optional

or

mandatory

in

almost

all

relevantstandards

30



Iterativemessage

passing

Largenumberof identicalcomputationalunits,operating

inparallel exploitsresourcesavailablefromscaling

Differentstandardsuse

differentcodes

Differentcodesrequired

withineachstandard

Computationaleffort

acrossstandards

spans

3ordersof magnitude

Computationaleffortperbitremainsalmostconstant

31



Referencedesign:

LDPC

decoder

for

IEEE

802.11n

208MHzclockfrequency

780Mbpsthroughput

3.4mm2 siliconarea

Workload~50100GOps

32

3.9 nJ/bit

2.3W @ 600Mbps

180nm



Referencedesign:

LDPC

decoder

for

IEEE

802.11n

208MHzclockfrequency

780Mbpsthroughput

3.4mm2 siliconarea

Workload~50100GOps

Max.throughput almost

doubles withhalf siliconarea

Canwedostillbetter??

33

3.9 nJ/bit2.3W @ 600Mbps

180nm

600 pJ/bit360mW @ 600Mbps

90nm

6.4x better

energy efficiency

Constant

throughput

Technologyscalingprovidessignificantenergysavings



VoltageFrequency

Scaling:

make

things

worse

to

make

them

better

Designacircuitthatworksfasterthanplanned(e.g.,byreplication)

• Whenrunningatthesamespeedandvoltage,energyefficiencybecomesworse

Utilizethefactthat

• Reducevoltageuntilit justmeetsthedelayconstraint

34

/N



VoltageFrequency

Scaling:

make

things

worse

to

make

them

better



Utilizethefactthat


35

/N

1/N

better

energy

efficiency



VoltageFrequency

Scaling:

make

things

worse

to

make

them

better



Utilizethefactthat


36

/N

1/N

better

energy

efficiency



37



Productiontest

is

needed

Identifychipswith

productiondefects

Classifyfunctional

dies

accordingtothespeed

theycanreach

Microprocessors:functionaldiessoldat

differentpricesdependingontheirspeed

CommunicationASICs:

need

to

run

at

apredefined

fixed

clock

speed

• Slowdiesmustbediscarded

• Fastdiesdonotexploitbetterperformance

38

M a n u f a c t u r i n g

P r o

d u c t i on

t e s t

Yield target

>95%

Speed binningimproves yield



path delay

#

o f o

c c u r a n c e s

VDD=nominal

VDD=low

target delay

target delay

VoltageScaling

Quadraticpowersavings

x Increasesmeandelaymaking

circuitsslower

x Increasesalsodelayvariance

makinghardertomeettargetperformance

ConventionalSolution

Overdesign:assumepessimistic

guardbands(timing,voltage)

x Higherpowerconsumptionon

average

x Limitthereturnsperformance,power)fromtechnologyscaling

39

130nm 90nm 65nm 45nm 32nm

Supply voltage approaches thethreshold voltage

More

dies

may

fail

to

meettargetperformance



40

6 bits

4 streams

108 tones every 3.6 ms

48 tones every 4 sm1 stream

1 bit

6 Mbps

600 MbpsMIMOdetector

Channeldecoder

arrival rate(bandwidth & CP length) bits/tone

PHYthroughput

MIMOdetector

Channeldecoder

SNR

(distance)

ErrorrateThroughput

(rate)

Rate adaptation is routinely used to deal with constantly varying channel conditions



41

6 bits

4 streams

108 tones every 3.6 ms

48 tones every 4 sm1 stream

1 bit

6 Mbps

600 MbpsMIMOdetector

Channeldecoder

arrival rate(bandwidth & CP length) bits/tone

PHYthroughput

MIMOdetector

Channeldecoder

SNR

(distance)

ErrorrateThroughput

(rate) Put thisscalability toservice for better energy efficiency and

toleranceagainst processvariations

Rate adaptation is routinely used to deal with constantly varying channel conditions



42

Iterativereceivers

/decoders:

data

passesmultipletimesthroughthe

samealgorithm

Performance

improves

with

each

iteratrion

Deminishing returns afterfew iterations

Achieve same rate only ata shorter distance

Achievable rate decreases



43

Iterativereceivers

/decoders:

data

passesmultipletimesthroughthe

samealgorithm

Performance

improves

with

each

iteratrion

Deminishing returns afterfew iterations

Achieve same rate only ata shorter distance

Achievable rate decreases

Yield improvement:exploit scalability toretain functionality under process

variations



Basebandprocessor

is

comprised

of

logic

and

memory(onchipandsometimesoff chip)

PredictionfromITRSroadmap:

Primaryconcern:embedded(small mediumsize)

memories

in

DSP

blocks Occupyasignificantpercentageof thearea

Consumeasignificantshareof thepower

Memoriesaretheprimarysourceof failure(yieldloss)

44

Memorybecomes

dominantissue



Manufacturingcircuits(memories)thatareactually

functionalandrobustbecomesincreasinglydifficult

45

Denserandlargermemoriesaremoresusceptibletoradiation

Processvariationleadstostaticerrorsanddysfunctionalcells

Reduced

noise

margins

and

supply

noise

induce

errors

in

weak

cells



Faulttolerant

by

design:

Channelfading(randomfluctuationof

signalstrength)

Unknown(noisy)channelparameters

Thermalnoise

and

interference

Systemlevelmechanismstorestore

reliablebehavior:

Forwarderrorcorrectioncoding

Automaticrepeatrequest

Applicationlevelfaulttolerance

(e.g.,

video

over

UDP)

46



47

ProposedParadigm

Relaxyieldrequirement(forinherentlyresilientsystems)

Selldieswithlimitedamountof defects(brokenmemorycells)

ConventionalParadigm

100%reliability,

accept

area

&

poweroverheadSellonlydefectfreedies



Conventionalyield

definition

Acceptingonlychipswithnodefects

ProposedyielddefinitionY(Nf )forsystemswithinherenthardwareerror

resilience

Chipswith

at

most

Nf faulty

memory

cells

pass

inspection

48

Accepting more defects means

Higher yield, and/or

Lower voltage & power

What is the impact on system-

level metrics (throughput) ?



Example :Communication

system with bit

interleaved

coded modulation (BICM)

HSPDA.WiMAX,3GPPLTE,GSM,WLAN,…

49

Interleaver memory:stores

reliability information of thereceived data bits

Faultmodel

:de

interleaver

built

from

unreliablememory(5%BER)

Binarysymmetric channel

Randomized error locations

0 5 10 15 200

1

2

3

4

5

6

7

8

SNR [dB]

M a x .

A c h i e v a b l e R a t e [ b p c u ]

Collaboration with TU-Vienna (Matz, Novak)



Example :Communication

system with bit

interleaved

coded modulation (BICM)

HSPDA.WiMAX,3GPPLTE,GSM,WLAN,…

50

Interleaver memory:stores

reliability information of thereceived data bits

Faultmodel

:de

interleaver

built

from

unreliablememory(5%BER)

Binarysymmetric channel

Randomized error locations

0 5 10 15 200

1

2

3

4

5

6

7

8

SNR [dB]

M a x .

A c h i e v a b l e R a t e [ b p c u ]

Unreliablecircuit behavior canbeincorporated intothe performanceanalysisof communicationsystems

Collaboration with TU-Vienna (Matz, Novak)



51

Explorethe

resilience

limits of

wirelesscommunication systems to

hardwaredefects

Simulation

of

complete

HSPA+

system,witherrorinjection(in

HARQ memory)

Inject

‘CircuitErrors’

Forvariousdefectrates(Nf )

creatememoryinstances

withrandomfaultlocations

T r a n s m i t t e r

HSPA+ System LLR StorageHARQ memory:

Bitflipsatrandomlocations



52

20errors(Nf =0.01%,200kbLLRstorage)

(Almost)same

throughput

as

for

defect

free

hardware

2’000errors(Nf =1%)

Achieverequiredthroughput(butclearpenaltyw.r.t.defectfreehardware)

Powerreductionbyallowinglowvoltages(~200mVless)



NewAlgorithmsandArchitecturesforBypassingthe

ExponentialComplexityAssociatedwithSpectralEfficiency

ImprovingEnergyEfficiency(nJ/bit)andAchieving

EnergyProportionality

in

Communications

ExploitingSystem

Level

Error

Tolerance

to

Cope

with

the

Issuesof DeppSubmicronIntegration

53



54

Signalprocessing algorithms:MIMOdetection,sparsechannel estimation,equalization,

CSADCs

for spectrum sensing

Systemdesignand test (prototypeimplementations):MIMO,visible light communication,

communication over plastic optical fibers,GSM/Evolved EDGE,TDSCDMA

VLSI

circuits

for

communications:

circuit

techniques

for

low

power

and

ultra

high

speed

signalprocessing,faulttolerantsignalprocessingfordeepsubmicronVLSI,VLSIfor

embeddedsystems

[email protected]

http://tcl.epfl.ch/

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