cooperative cross-layer protection for resource

COOPERATIVE CROSS-LAYER PROTECTION FOR RESOURCE CONSTRAINED MOBILE MULTIMEDIA SYSTEMS

Kyoungwoo Lee (final defense)

Prof. Nikil DuttProf. Nalini VenkatasubramanianProf. Lichun Bao

Nov. 26, 2008

Contents

Thesis MotivationThesis Proposal – Cooperative, Cross-layer Methods

PPC (Partially Protected Caches)EAVE (Error-Aware Video Encoding)CC-PROTECT (Cooperative, Cross-layer Protection)

Thesis Contribution and Future Direction

2

Mobile Multimedia Embedded Systems3

Web Browsing

Image Browsing

Satellite TVVideo Streaming

Animation

Video Conferencing

Map Routing

Mobile TV

3D GraphicsResource-limited mobile devices!Main problem is to achieve low power with high performance, high QoS, and high reliability

Reliability

Reliability is an emerging and critical concern in mobile devicesNew enhanced technology makes devices vulnerable to errors due to high complexity and high integration

Exponential increase of soft error rate as technology scales [Baumann, 05]Mobile applications are running close to humans

In pervasive computing, failures of healthcare mobile devices cause serious results

Redundancy techniques incur high overheads of power and performanceTMR (Triple Modular Redundancy) may exceed 200% overheads without optimization [Nieuwland, 06]

Challenging to optimize multiple properties (e.g., performance, power, QoS, and reliability) in mobile embedded systems

4

Soft error is becoming an every second concern!Soft Error Rate (SER) – FIT (Failures in Time) = number of errors in 109 hours

5

SER (FIT) MTTF Reason

1 Mbit @ 0.13 µm 1000 104 years


1 Mbit @ 0.13 µm 1000 104 years64 MB @ 0.13 µm 64x8x1000 81 days High Integration



128 MB @ 65 nm 2x1000x64x8x1000 1 hour Technology scaling and Twice Integration




A system @ 65 nm 2x2x1000x64x8x1000 30 minutes Memory takes up 50% of soft errors in a system





A system with voltage scaling @ 65 nm

100x2x2x1000x64x8x1000

18 seconds Exponential relationship b/w SER & Supply Voltage





A system with voltage scaling @ 65 nm

100x2x2x1000x64x8x1000

18 seconds Exponential relationship b/w SER & Supply Voltage

A system with voltage scaling @ flight (35,000 ft) @ 65 nm

800x100x2x2x1000x64x8x1000 FIT

0.02 seconds

High Intensity of Neutron Flux at flight (high altitude)

Errors and Failures in Mobile Embedded Systems

Faults or Errors can cause Failures6

Application

Middleware/ OS

Hardware

Network

Soft Error

PacketLoss

Bug

Exception

Errors and Error Control Schemes at Hardware

7

Failures Causes Metrics Traditional ApproachesSoft Errors, Hard Failures, System Crash

External Radiations, Thermal Effects, Power Loss, Poor Design, Aging

FIT, MTTF, MTBF

Spatial Redundancy (TMR, Duplex, RAID-1 etc.) and Data Redundancy (EDC, ECC, RAID-5, etc.)

•FIT: Failures in Time (109 hours)•MTTF: Mean Time To Failure•MTBF: Mean Time b/w Failures•TMR: Triple Modular Redundancy•EDC: Error Detection Codes•ECC: Error Correction Codes•RAID: Redundant Array of Inexpensive Drives

Hardware failures are increasing as technology scales(e.g.) SER increases by up to 1000 times [Mastipuram, 04]

Redundancy techniques are expensive(e.g.) ECC-based protection in caches can incur 95% performance penalty [Li, 05]

Application

MW/ OS

Hardware

Network

Errors and Error Control Schemes at Software

8

Failures Causes Metrics Traditional ApproachesWrong outputs, Infinite loops, Crash

Incomplete Specification, Poor software design, Bugs, Unhandled Exception

Number of Bugs/Klines, QoS, MTTF, MTBF

Spatial Redundancy (N-version Programming, etc.), Temporal Redundancy (Checkpoints and Backward Recovery, etc.)

•QoS: Quality of Service

Software errors become dominant as system’s complexity increases(e.g.) Several bugs per kilo lines

Hard to debug, and redundancy techniques are expensive(e.g.) Backward recovery with checkpoints is inappropriate for real-time applications

Application

MW/ OS

Hardware

Network

Errors and Error Control Schemes in Networks

9

Failures Causes Metrics Traditional ApproachesData Losses, Deadline Misses, Node (Link) Failure, System Down

Network Congestion, Noise/Interference, Malicious Attacks

Packet Loss Rate, Deadline Miss Rate, SNR, MTTF, MTBF, MTTR

Resource Reservation, Data Redundancy (CRC, etc.), Temporal Redundancy (Retransmission, etc.), Spatial Redundancy (Replicated Nodes, MIMO, etc.)

•SNR: Signal to Noise Ratio•MTTR: Mean Time To Recovery•CRC: Cyclic Redundancy Check•MIMO: Multiple-In Multiple-Out

Network is unreliable (especially, wireless networks)Joint approaches across OSI layers have been investigated for minimal costs [Vuran, 06][Schaar, 07]

Application

MW/ OS

Hardware

Network

Conventional Approaches

Most redundancy techniques incur overheads in terms of performance, power, area, etc.

Conventional TRM (Triple Modular Redundancy) can incur 200% overheads without optimization.Backward Recovery with Checkpoints cannot guarantee the completion time of a task.

Recently proposed techniques have focused on the cost reduction without losing reliability

However, they still incur overheads

10

Thesis Problem Statement

Study tradeoffs among system properties(e.g.) Redundancy incurs energy overheads while DVS increases SER significantly

Examine errors and error control schemes across system abstraction layers

(e.g.) network errors & error-resilient video encoding, soft errors & ECC or EDC, etc.

Maximize reliability with minimal costs of power and performance for mobile embedded systems

11

Cross-Layer MethodsCross-layer approaches:

aim at system-level optimizationIntegrate and coordinate techniques across system layers

Classification [Srivastava, 05]

Top-down, Bottom-up, or Both direction Top-down – PPC, PDVS [GRACE], etc.Bottom-up – EAVE, etc.Both direction – CC-PROTECT, etc.

Coupling or Merging layers Dynamo [Mohapatra], xTune [Kim], etc.

12

Top-down

Bott

om-u

p

CouplingM

erging

Cross-Layer Approaches – GRACE

GRACE project @ UIUC [W. Yuan Ph.D. thesis in ’04 and A. F. Harris III, Ph.D. thesis in ’06]

QoS/Power tradeoffsPrimarily OS adaptation for power management in multimedia mobile devicesNetwork adaptation for power management in multimedia communications

13

[GRACE, 05]

Application

Operating

System

Hardware

Cross-Layer Approaches – DYNAMO & FORGE

DYNAMO middleware for FORGE project @ UCI [S. Mohapatra Ph.D. thesis in ’05 and R. Cornea Ph.D. thesis in ’07]

QoS/Power tradeoffs for mobile embedded systemsMiddleware-driven coordination and proxy-based cooperation1. Content transcoding at the

application layer2. Network traffic shaping at the

network layer3. Backlight (LCD display) setting at

the hardware layer4. NIC shutdown, CPU DVS/DFS at

the hardware layer

14

Application

Middleware/ OS

Hardware

Proxy Server

(NW & MW)

12

3 4

Cross-Layer Approaches – xTune

xTune framework @ UCI and SRI [M. Kim Ph.D. thesis in ’08]QoS/Power/Timeliness adaptation for distributed real-time embedded systemsA Formal Methodology for cross-layer tuning and verifiable timeliness of Mobile Embedded Systems

15

Handheld Server

Proxy Server

Application

Middleware/ OS

Hardware

Thesis Proposed Contribution

Thesis proposes a cross-layer design methodology for mobile multimedia embedded systems with minimal costs

Reliability/QoS/Power/Performance system optimization for mobile multimedia systems

Cooperative, Cross-Layer ProtectionPPC, EAVE, & CCPROTECTLow-cost reliability

16

Overview of Thesis Proposals17

Hardware

UnprotectedCache

ProtectedCacheProtectedCache ECCECC

Error-prone Networks

Mobile Video Application



EAVE

Error-ResilientEncoder (e.g., PBPAIR)

Error-Controller(e.g., frame drop)Error-Controller

(e.g., frame drop)

OriginalVideo

Error-AwareVideo

Monitor & Translate SER

MW/OS

Packet Loss

Frame Drop

Error detection

Application

Multimedia Application

EDCEDC

Correction

QoSPPC (Partially Protected Caches)EAVE (Error-Aware Video Encoding)CC-PROTECT (Cooperative, Cross-layer Protection)

Contents


PPC (Partially Protected Caches)EAVECC-PROTECT


18

Application

Hardware

Middleware/ OS Network

Conventional Protection for Caches

Conventional Protected CachesUnaware of fault tolerance at applicationsImplement a redundancy technique such as ECC to protect all data for every access

Overkill for multimedia applicationsECC (e.g., a Hamming Code) incurs high performance penalty by up to 95%, power overhead by up to 22%, and area cost by up to 25%

High Cost

CacheCache ECCECCU

naware of Application

19

PPC (Partially Protected Caches)

ObservationNot all data are equally failure critical

Multimedia data vs. control variables

Propose PPC architectures to provide an unequal protection for mobile multimedia systems [Lee, CASES06][Lee, TVLSI08]

Unprotected cache and Protected cache at the same level of memory hierarchyProtected cache is typically smaller to keep power and delay the same as or less than those of Unprotected cache

UnprotectedCache

ProtectedCacheProtectedCache

Memory

PPC

20

PPC for Multimedia Applications

Propose a selective data protection [Lee, CASES06]Unequal protection at hardware layer exploiting error-tolerance of multimedia data at application layerSimple data partitioning for multimedia applications

Multimedia data is failure non-criticalAll other data is failure critical

Fault Tolerance

Power/D

elay Reduction

21

UnprotectedCache Protected

CacheProtectedCache

Memory

PPC

PPC for General Applications

DPExplore [Lee, PPCDIPES08]Explore partitioning space by exploiting awareness of vulnerability of each data page

Vulnerable timeIt is vulnerable for the time when eventually it is read by CPU or written back to Memory

Pages causing high vulnerable time are failure criticalVulnerable time closely estimates failure rate

Read

Write

Eviction

Incoming

data

t0 t1 t2 t3

22

UnprotectedCache Protected

CacheProtectedCache

Memory

PPC

Summary – PPCAll data are not equally failure criticalPropose a PPC architecture to provide unequal protection

Support an unequal protection at hardware layer by exploiting error-tolerance and vulnerability at applicationPresent cost-efficient reliability

Related Publications[Lee, CASES06] – PPC for multimedia embedded systems[Lee, PPCDIPES08] – PPC for general applications[Lee, TVLSI08] – PPC and design space exploration

Under submission[Lee, TODAES??] – PPC for general applications and instruction caches

23

Application Data & Code

Failure Non-Critical

Failure Critical

Unprotected Cache

Protected Cache

PPC

Page Partitioning Algorithms

Error-tolerance of MM dataVulnerability of Data & Code

FNC & FC are mapped into Unprotected & Protected Caches

Contents


PPCEAVE (Error-Aware Video Encoding)CCPROTECT


24

Application


Active Error Exploitation – Intentional Frame Drop



Enc

CPU

Tx

WNI

Dec

CPU

Rx

WNIFDT-1FDT-1 FDT-2FDT-2 FDT-3FDT-3

•FDT: Frame Drop Type•Enc: Encoding, Dec: Decoding•WNI: Wireless Network Interface

Intentional Frame Drop (one way to actively exploit errors) can result in energy reduction for each operationFDT-1 affects the following components with respect to power, performance, and QoS in mobile video applications

25

Packet Loss

Error-Aware Video Encoding

Propose EE-PBPAIR [Lee, DIPES08]

Intentionally drop frames at video encodingReduce the energy consumption for video encodingMaintain the video quality by exploiting error-resilience of PBPAIR


Packet Loss

Intentional frame drop

Error-Aware Video Encoder (EAVE)

Error-ResilientEncoder

(e.g., PBPAIR)

Error-Controller(e.g., frame dropping)Error-Controller

(e.g., frame dropping)

OriginalVideo

Error-Resilient

Video

•EIR: Error Injection Rate

26

Error-AwareVideo

Summary – EAVE

Intentional Frame Drop is one way to exploit errors activelyPropose an error-aware video encoding (EE-PBPAIR)

Present a knob (EIR) to adjust the amount of errors considering the QoS feedbackMaintain the video quality using error-resilience of PBPAIR

Related Publication[Lee, DIPES08] – EE-PBPAIR

Considering Submission[Lee, TECS??] – Generalized idea for error-resilient video encodings

•EIR: Error Injection Rate•PLR: Packet Loss Rate

27

Error Resilient Video Encoder

Error Controller

Hardware

MiddlewareEnergy

Reduction

CPU, Memory, and WNIC

Application

Network or Decoding Side

Error Rate = PlR + EIR

EIR PLR& QoS

Error-Aware Video Data

Contents


PPCEAVECC-PROTECT (Cooperative Cross-layer Protection)


28

Application

Hardware


Errors and Error Control Schemes – No Coupling

Different errors and their protection techniques have not been considered jointly

No coupling and no cooperation

Cooperating control schemes in a cross-layer manner can open a new venue

29



Application


Hardware Soft Error

PacketLoss

PPC still incurs overheads due to ECC-protection30

UnprotectedCache

ProtectedCacheProtectedCache

Memory

PPC

Propose PPC architectures to provide an unequal protection for mobile multimedia systems [Lee, TVLSI08]

Unprotected cache and Protected cache a the same level of memory hierarchy

PPC still incurs overheads due to high expensive ECC-protection at the protected cache

29% energy reduction compared to the protected cache

10% energy overhead compared to the unprotected cache

PBPAIR is energy-inefficient in error-free network

PBPAIR is error-resilient and energy-efficient in generalPBPAIR may not be energy efficient in case of error-free network

31

PBPAIR

PLR

PacketLoss

network

Intra_Threshold•PBPAIR: Probability-Based Power Aware Intra Refresh [Kim, 06]

Outline of CC-PROTECT32

frame K frame K+1

UnprotectedCache

ProtectedCacheProtectedCache PPCEDCEDC





Error-Aware Video Encoder (EAVE)

Error-ResilientEncoder (e.g., PBPAIR)

Error-Controller(e.g., frame drop)Error-Controller

(e.g., frame drop)

OriginalVideo

Error-AwareVideo

DFR (Drop &Forward Recovery)

BER (Backward Error Recovery)

Feedback

Monitor & Translate SER

Trigger Selective DFR

Support EAVE & PPC

Parameter

MW/OS

Packet Loss

Frame Drop

Error detection

QoS Loss

Energy SavingBASE = Error-prone video encoding + unprotected cache

HW-PROTECT = Error-prone video encoding + PPC with ECC

APP-PROTECT = Error-resilient video encoding + unprotected cache

MULTI-PROTECT = Error-resilient video encoding + PPC with ECC

CC-PROTECT1 = Error-prone video encoding + PPC with EDC

CC-PROTECT2 = Error-prone video encoding + PPC with EDC + DFR

CC-PROTECT = error-resilient video encoding + PPC with EDC + DFR

33

EDC impact17% Reduction compared to HW-PROTECT4% Reduction compared to BASE

EDC + DFR impact36% Reduction compared to HW-PROTECT26% Reduction compared to BASE

EDC + DFR + PBPAIR(CC-PROTECT) impact56% Reduction compared to HW-PROTECT49% Reduction compared to BASE

Summary – CC-PROTECTPropose CC-PROTECT approach, which cooperates existing schemes across layers to mitigate the impact of soft errors on the failure rate and video quality in mobile video encoding systems

PPC (Partially Protected Caches) with EDC (Error Detection Codes) at hardware layerDFR (Drop and Forward Recovery) at middlewarePBPAIR (Probability-Based Power Aware Intra Refresh) at application layer

Demonstrate the effectiveness of low-cost (about 50%) reliability (1,000x) at the minimal cost of QoS (less than 1%)Related Publication

[Lee, ACMMM08] – CC-PROTECTConsidering Submission

[Lee, ACMTOMCCAP??] – Tradeoff space exploration with CC-PROTECT

34

Application

Middleware/ OS

Hardware UnprotectedCache Protected

CacheProtectedCache

ECC

DFR -Error Correction

PBPAIR -Error Resilience

EDC

Contents


PPCEAVECC-PROTECT


35

Application

Hardware


Overall Thesis Contribution

Cross-layer methodology to design mobile multimedia embedded systems with minimal costs

36

Application

Middleware/ OS

Hardware

Network

Soft Error

PacketLoss

1. Effective Cross-layer approaches for reliability

2. Low-cost reliability3. Expanded trade-off

space 4. Extended applicability of

existing techniques

Effectiveness of Thesis Proposals (Energy Saving)

25% energy reduction, as compared to a conventional protected cache with ECC

30% energy reduction, as compared to a conventional video encoding

PPC EAVE

56% energy reduction, as compared to a conventional composition of protections

37

CCPROTECT

Publication38

[Lee, ACMMM08] K. Lee, A. Shirvastava, M. Kim, N. Dutt, and N. Venkatasubramanian, “Mitigating the impact of hardware defects on multimedia applications – A cross-layer approach”, In ACM International Conference on Multimedia, Oct. 2008.

[Lee, TVLSI08] K. Lee, A. Shrivastava, I. Issenin, N. Dutt, and N. Venkatasubramanian, “Partially protected caches to reduce failures due to soft errors in multimedia applications”, In IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 2008, to appear.

[Lee, DIPES08] K. Lee, M. Kim, N. Dutt, and N. Venkatasubramanian, “Error exploiting video encoder to extend energy/QoS tradeoffs for mobile embedded systems”, In 6th IFIP Working Conference on Distributed and Parallel Embedded Systems (DIPES), Sep. 2008.

[Lee, PPCDIPES08] K. Lee, A. Shrivastava, N. Dutt, and N. Venkatasubramanian, “Data partitioning techniques for partially protected caches to reduce soft error induced failures”, In 6th IFIP Working Conference on Distributed and Parallel Embedded Systems (DIPES), Sep. 2008.

[Lee, CASES06] K. Lee, A. Shrivastava, I. Issenin, N. Dutt, and N. Venkatasubramanian, “Mitigating soft error failures for multimedia applications by selective data protection”, In Int. Conference on Compilers, Architecture, & Synthesis for Embedded Systems (CASES), Oct. 2006.

[Lee, ICME05] K. Lee, N. Dutt, and N. Venkatasubramanian, “Experimental Study on Energy Consumption of Video Encryption for Mobile Handheld Devices", In IEEE International Conference on Multimedia and Expo (ICME 05), Poster Session, July 2005.

[Mohapatra, IPDPS05] S. Mohapatra, R. Cornea, H. Oh, K. Lee, M. Kim, N. Dutt, R. Gupta, A. Nicolau, S. Shukla, and N. Venkatasubramanian, “A cross-layer approach for power-performance optimization in distributed mobile systems”, In Next Generation Software Program in conjunction with IEEE International Parallel and Distributed Processing Symposium (IPDPS), April 2005.

Application

Middleware/ OS

Hardware

Network

[Lee, TVLSI08][Lee, PPCDIPES08][Lee, CASES06]

[Lee, DIPES08]

[Lee, ACMMM08][Mohapatra, IPDPS05][Lee, ICME05]

Future Direction

Error Rate Translation/Integration

Different types of errorsDifferent components across system layers

Cross-layer methods for distributed embedded systems (Horizontal Expansion)

Network-aware methodsContext-aware approaches

39



Application

Middleware/ OS

Hardware

Network

Soft Error

PacketLoss

Bug

Exception

Thank you! Any Questions or Comments?

40

Backup Slides41

Why Cross-Layer Approach?Cross-layer interactions and conflicts arise between system properties

DVS increases SER exponentiallyOver protection or under protection

All ECC for multimedia data is an overkillCross-layer approaches can maximize the reliability with minimal power and performance overheads

Benefits of Cross-layer approachesGlobal system viewCoordination for intelligent selectionAdaptation

Cross-layer approaches have been promising to save the resources at the cost of QoS [Mohapatra, 05][Yuan, 04]

•DVS: Dynamic Voltage Scaling•SER: Soft Error Rate•ECC: Error Correction Codes•QoS: Quality of Service

42

Thesis Proposed Contribution: CC-PROTECT

Cooperative Cross-layer Protection (CC-PROTECT) by exploiting error-awareness and error control schemes across system abstraction layersContribution

Present cost-efficient reliability methods (cooperative cross-layer protection)Open expanded tradeoff spaces and operating pointsRediscover applicability of existing approaches for other purposes

43

Performance vs. Capacity44

Total energy available from a battery is a design issue and is fixed at a design time, along with its weight and sizeStark contrast between linear growth rate of battery capacity and exponential technology improvement rate of system components

[Udani] Sanjay Udani and Jonathan Smith, “Power management in mobile computing”

Generalized Fault Tolerance Techniques

1) Modular Redundancy2) N-Version Programming3) Error-Control Coding4) Checkpoints and Rollbacks5) Recovery Blocks

45

[Chetan, SPC04] S. Chetan, A. Ranganathan, and R. Campbell, “Towards Fault Tolerant Pervasive Computing”, in SPC ’04[Somani, IEEECom97] A. K. Somani and N. H. Vaidya, “Understanding Fault Tolerance and Reliability”, in IEEE Computer ’97 vol. 30 issue 4

1) Modular Redundancy

Modular RedundancyMultiple identical replicas of hardware modulesVoter mechanism

Compare outputs and select the correct output

Tolerate most hardware faultsEffective but expensive

ConsumerData

Producer Bvoter

Producer Afault

46

2) N-version Programming

N-version ProgrammingDifferent versions by different teams

Different versions may not contain the same bugs

Voter mechanismTolerate some software bugs

Producer A ConsumerData

voter

Program i Program j

Programmer K Programmer L

fault

47

3) Error-Control Coding

Error-Control CodingReplication is effective but expensiveError-Detection Coding and Error-Correction Coding

(example) Parity Bit, Hamming Code, CRC

Much less redundancy than replication

Producer A Consumer

Data

ErrorControl

Datafault

48

4) Checkpoints & Rollbacks

Checkpoints and RollbacksCheckpoint

A copy of an application’s stateSave it in storage immune to the failures

RollbackRestart the execution from a previously saved checkpoint

Recover from transient and permanent hardware and software failures


Application

state (K-1) state K

faultCheckpoint

Rollback

State K

49

5) Recovery Blocks

Recovery BlocksMultiple alternates to perform the same functionality

One Primary module and Secondary modules Different approaches

1) Select a module with output satisfying acceptance test

2) Recovery Blocks and RollbacksRestart the execution from a previously saved checkpoint with secondary module

Tolerate software failures


state (K-1) state K

faultCheckpoint

Rollback

Block XBlock YBlock Z

Block X2

Application

50

Soft Errors (Transient Faults)

SER increases exponentially as technology scalesIntegration, voltage scaling, altitude, latitude

Caches are most hit due to:Larger portion in processors (more than 50%) No masking effects (e.g., logical masking)

Transistor

01

5 hours MTTF

1 month MTTF

Intel Itanium II Processor

•MTTF: Mean time To FailureBit Flip

51

[Baumann, 05]

Related Work

Process Technology SolutionsHardening [Baze, IEEE Trans. on Nuclear Science 00]SOI [O. Musseau, IEEE Trans. on Nuclear Science 96]Process complexity, yield loss, and substrate cost

Microarchitectural Solutions for Caches

Cache Scrubbing [Mukherjee, PRDC04]Low Power Cache [Li, ISLPED04]Area Efficient Protection [Kim, DATE06]Multiple Bit Correction [Neuberger, TODAES 03]Cache Size Selection [Cai, ASP-DAC06]In-Cache Replication [Zhang, DSN03]Replication Cache [Zhang, IEEE Computers 05]High overheads in terms of power, performance, and area

52

Our Solution-Protects caches from failures due to soft errors exploiting error-tolerance of applications-Protection can be in conjunction with any techniques

Our Solution-Protects caches from failures due to soft errors exploiting error-tolerance of applications-Protection can be in conjunction with any techniques

Unequal Data Protection

All pages are not equally failure critical

Multimedia data is failure non-criticalProgram variables are failure criticalFailures: system crash, infinite loop, segmentation faults, etc

QoS degradation is not a failure

Only 9 pages out of 83 are failure critical

53

Failure Critical and Failure Non-Critical Data54

Soft Errors on Increase55

Increase exponentially due to technology scaling0.18 µm

1,000 FIT per Mbit of SRAM

0.13 µm 10,000 to 100,000 FIT per Mbit of SRAM

Voltage ScalingVoltage scaling increases SER significantly

SER ∝ Nflux CSx expQcritical{-x

Qs}

where Qcritical = C Vx

Experimental Setup for Page Failure Rates56

Experimental Framework57

Experimental Results – Failure Rate

Failure rate of PPC is close to that of Safe (Safe is a protected cache configuration with an ECC protection, i.e., protecting all data, and Unsafe is an unprotected cache)

58

Experimental Results – Performance

Runtime of PPC is close to that of Unsafe

59

Experimental Results – Power

Energy consumption of PPC is close to that of Unsafe

60

Experimental Setup for DPExplore61

DPExplore Results62

Video Encoding63

Error-Resilient Video Encoding

Error-resilient video encodings have been developed to combat errors in networks

PBPAIR – energy-efficient and error-resilient video encoding [Kim,06]Passive Error Exploitation

It compresses video data according to PLR



Packet LossMaintain the QoSEmbed Error-Resilience

against packet losses

64

•PBPAIR: Probability-BasedPower Aware Intra Refresh

NetworkResilience

PLRParameters

65

Related Work

Energy/QoS-aware video encoding

Video encoding parameters [Mopatra, IPDPS05]

Motion estimation algorithm [Tourapis, VCIP00]

Integrated power management [Mohapatra, ACM MM03]

Global cross-layer adaption [Yuan, MMCN04]

Transmission power and QoS [Eisenberg, IEEE Trans. on CSVT 02]

Not consider error-resilience

Error-resilient video encodingError-resilient GOP [Yang, JVCIP07]

AIR (Adaptive Intra Refreshing) [Worral, ICASSP01]

PGOP (Progressive GOP) [Cheng, PCS04]

PBPAIR (Probability-Based Power Aware Intra Refresh) [Kim, MCCR06]

Passive error exploitation

Our Solution-Error-aware video encoding: exploits errors actively to minimize energy consumption

Our Solution-Error-aware video encoding: exploits errors actively to minimize energy consumption

EE-PBPAIR66

Experimental Setup67

Experimental Results – Energy Reduction

Energy saving occurs at every component in a path from encoding to decoding in mobile video applications

EC= Energy ConsumptionEnc EC= EC for EncodingTx EC= EC for TransmissionDec EC= EC for DecodingRx EC= EC for Receiving

68

•PSNR: Peak Signal to Noise Ratio

PLR = 10% and EIR = 10%

Experimental Results – Expanded Tradeoff Space 69

Experimental Energy Saving70

•Source EC = Enc EC + Tx EC•Destination EC = Rx EC + Dec EC

Experimental Results – Adaptive EIR

Feedback-based approach (Adaptive EE-PBPAIR) maintains the required video quality compared to Static EE-PBPAIR

71

Adaptive EIR72

Conclusion

Studied two main cross-layer approaches

PPCEAVE

Demonstrated the effectiveness of our cooperative cross-layer approaches by exploiting error tolerance and error control schemes

NetworkEIR

FLR

Resilience

PLRfeedback

73

Tolerance

UnequalProtection

Failure Rate74

Video Quality75

Memory Access Time (performance)76

Future DirectionCooperative approaches combining PPC and EAVE

Middleware-driven cross-layer approach manages error control schemesTranslate errors to exploit existing approaches at other abstraction layers

PPCApply our approach for other components

Instruction caches and logics

EAVEIntelligent frame dropping techniques

To maximize the energy saving while minimizing the quality degradation

77

EIR

FLR

Resilience

PLRfeedback

Tolerance

UnequalProtection

SER

Thesis Outline

Thesis proposes a cross-layer methodExploit errors and error control schemes across layers to maximize reliability with minimal costs for mobile embedded systems

Topic 1 – Approach at hardware and application layersPPC (unequal data protection at hardware exploiting error tolerance at application) [Lee, CASES06][Lee, DIPES08][Lee, TVLSI08]

Topic 2 – Approach at application, middleware, and network layersEAVE (intentional exploitation of errors at application, incorporating error resilience in networks) [Lee, DIPES08]

Topic 3 – Approach across application/middleware-OS/HWCC-PROTECT (middleware-driven cooperative exploitation of errors and error control schemes across layers) [Lee, ACM MM 08]

78

Application

Hardware


References (cross-layers and tools)[Bajic, 07] I. V. Bajic. Efficient cross-layer error control for wireless video multicast. 53(1):276–285, Mar 2007.

[Dynamo] DYNAMO. Power Aware Middleware for Distributed Mobile Computing. University of California at Irvine, http://dynamo.ics.uci.edu/.

[Forge] FORGE Project. A Framework for Optimization of Distributed Embedded Systems Software. University of California at Irvine, http://www.ics.uci.edu/~forge/.

[Grace] GRACE Project. Global Resource Adaptation through CoopEration. University of Illinois at Urbana-Champaign, http://rsim.cs.uiuc.edu/grace/.

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