Tuning soc platforms for multimedia processing identifying limits and tradeoffs
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Tuning SoC Platforms for Multimedia Processing: Identifying Limits and Tradeoffs. Samarjit Chakraborty Joint work with Alexander Maxiaguine (ETH Zurich) Yongxin Zhu and Weng-Fai Wong. Background and Motivation. SoC platforms for multimedia processing

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Tuning SoC Platforms for Multimedia Processing: Identifying Limits and Tradeoffs

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Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Tuning SoC Platforms for Multimedia Processing: Identifying Limits and Tradeoffs

Samarjit ChakrabortyJoint work withAlexander Maxiaguine (ETH Zurich) Yongxin Zhu and Weng-Fai Wong


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Background and Motivation

  • SoC platforms for multimedia processing

    • Example: Eclipse template and Viper SoC architecture

  • Advantages and Disadvantages

    • Flexibility, low design costs, time-to-market advantages …

    • Large disparity in performance compared to customized

      ASIC-based solutions

  • Using configurable platforms

    • Tuning, platform configuration and management techniques to

      improve performance involves several tradeoffs


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Video decoding

Network

Interface

PE1

PE2

Vout

Bv

B1

B2

VLD

IQ

IDCT

MC

MP3

B3

Ba

Aout

Audio decoding

Network

Example Platform Architecture

buffer

sizes

scheduling

policy

A set-top box device

  • Most design space exploration techniques rely on simulation

    • Artemis project: trace-driven co-simulation and symbolic execution

      of applications


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

simulate system

& estimate parameters

choose

configuration

parameters

choose

configuration

parameters

generate

design point

generate

design point

simulate

system

analytically

evaluate

system

Exploring the Platform Configuration Design Space

purely simulation

oriented approach

resorting to simulation

only once at the beginning


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Exploring the Platform Configuration Design Space

Analytical framework to identify tradeoffs between platform configurations and management techniques

Main tool: A new technique to capture the variability in multimedia workloads

  • Outline of the talk:

  • Main difficulty

  • Description of the technique

  • A case study


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Modeling a Platform Architecture

infinite sequence

of “stream objects”

processed

“stream objects”

network

playout

buffer

buffer

output

device

processor

  • Task structure

    • A set of concurrently executing tasks that exchange information

      through unidirectional data streams


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Tuning SoC Platforms: Main Challenge

network

complex and bursty on-chip traffic

  • Reasons:

    • high data-dependent variability in execution time requirements

    • variability in input-output rates associated with tasks

      (e.g. variable length decoding in MPEG-2)


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

How do we Capture this Variability?

  • Reasons:

    • high data-dependent variability in execution time requirements

    • variability in input-output rates associated with tasks

      (e.g. variable length decoding in MPEG-2)

Model?

  • Statistical methods (e.g. Varatkar & Marculescu TVLSI’04)

  • Deterministic best/worst case characterization


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

worst-case

execution time

best-case

execution time

Best/Worst Case Characterization?

  • Classical real-time systems

    • e is the best/worst case execution requirement of one stream

      object, then for k objects, it is ke

  • Can we provide a better bound better?

    • Use “variability characterization curves” (VCCs)


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

code

Variability Characterization Curves (VCCs)

sequence of

input

stream objects

sequence of

input

stream objects

  • Each execution of the code

    • consumes variable number of stream objects

    • produces a variable number of stream objects

    • requires a variable number of processor cycles


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

code

Variability Characterization Curves (VCCs)

sequence of

input

stream objects

sequence of

input

stream objects

  • Arrival of stream objects at the input is bursty

  • The processor also might not be always available (because of some

  • other tasks or multiple streams being processed on it)


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Variability Characterization Curves (VCCs)

  • Each execution of the code

    • consumes variable number of stream objects

    • produces a variable number of stream objects

    • requires a variable number of processor cycles

consumption curve

production curve

workload curve

  • Arrival of stream objects at the input is bursty

  • The processor also might not be always available (because of some

  • other tasks or multiple streams being processed on it)

arrival curve

service curve


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Variability Characterization Curves (VCCs)

  • Best/worst-case characterization of sequences

  • Consumption/production/workload curves

    • sequences of consecutive executions of the code

  • Arrival/service curves

    • sequences of consecutive time units

Record the max and min in this window


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

max execution time for any

sequence of 50 events = 200

sum

upper workload curve

event #

sequence length

Example: Workload Curve

execution time

event #

max difference


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Example: Workload Curve

Same long-term behavior, but

different burstiness on smaller time scales


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Example: Service Curve

A VCC represents a family of instances

maximum/minimum computing power in any interval of length 2

computing power

in time interval [0,2]


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Determining System Properties

Computing with Curves

service

input

stream

output

stream

PE

remaining service


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

arrival

max. delay

max. memory

Determining System Properties

  • Max/Min buffer fill level

  • Max/Min delay

  • Utilization

  • ….


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

e1

e3

e4

e2

Proportional

Share

Rate Monotonic

CPU2

CPU1

Determining System Properties

?

P1=7

?

P2=11

Periodic stream

Execution

requirement

(can be represented as

a workload curve)


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

remains

periodic

becomes

bursty

e3

e1

e4

e2

burstiness

increases

Rate Monotonic

Proportional Share

becomes

bursty

input streams

Determining System Properties


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Video decoding

Network

Interface

PE1

PE2

Vout

Bv

B1

B2

VLD

IQ

IDCT

MC

MP3

B3

Ba

Aout

Audio decoding

Network

Case Study

buffer

sizes

scheduling

policy

A set-top box device

  • Tradeoffs between TDMA period and buffer sizes

    • Large period  low overhead but larger buffers

    • Small period  high overhead but smaller buffers


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

Case Study

buffer space

[#bits x 107]


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

SDRAM

RISC

Arbiter

DSP

The Bottomline

Communication Templates

Computation Templates

imagecoprocessor

FPGA

Characterize using VCCs

(instruction set simulation / cycle accurate simulation / databook)

DSP

RISC

SDRAM

CANinterface

mC

Architecture

Tune/Configure

TDMA

EDF

EDF

proportionalshare

TDMA

FCFS

WFQ

Discrete event simulation is NOT required

Use the proposed method!

Priority

dynamicfixed priority

static

WFQ


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

simulate system

& estimate parameters

choose

configuration

parameters

generate

design point

analytically

evaluate

system

The Bottomline


Tuning soc platforms for multimedia processing identifying limits and tradeoffs

?

?

?

?

?

Questions!


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