p. marwedel: embedded software:how to make it efficient? slide -1 - embedded software: how to make...

P. Marwedel: Embedded Software:How to make it efficient? Slide -1 -

Embedded Software:How to make it efficient?

Peter Marwedel University of Dortmund

Informatik 12 44221 Dortmund, Germany


What is an embedded system?

These are not the embedded systems we will talk about!


Embedded Systems

Main reason for buying is not information processingCharacteristics:• not recognised as information processing• frequently real-time behaviour required • must be dependable & guarantee privacy• many of these systems are mobile systems• fundamental technology for pervasive computing/

ambient intelligence, implemented in complex software

Embedded systems = information processing systems embedded into a larger product


Views on embedded software

For many products in the area of consumer electronics the amount of code is doubling every two years [Fritz Vaandrager in: Rozenberg, Vaandrager (eds.): Lectures on Embedded Systems, LNCS, Vol. 1494, 1998]

„On Nanoscale Integration and Gigascale Complexity in the Post - .com world“ [de Man, Keynote, DATE 2002]

... it is now common knowledge that more than 70% of the development cost for complex systems such as automotive electronics and communication systems are due to software development[A. Sangiovanni-Vincentelli, 1999]


The energy/flexibility conflict- Intrinsic Power Efficiency -

Technology

[H. de Man, Keynote, DATE‘02;T. Claasen, ISSCC99]

Operations/Watt[MOPS/mW]

Processors

Reconfigurable Computinghardwired muxed

1

0.1

0.01

0.13µ

Necessary to optimize software; otherwise the prize for software flexibility cannot be paid!

Necessary to optimize software; otherwise the prize for software flexibility cannot be paid!

Ambient Intelligence

0.07µ

DSP-ASIPs

µPs

10

0.25µ0.5µ1.0µ

poor software generation techniques


„Power is considered as the most important constraint in embedded systems“[in: L. Eggermont (ed): Embedded Systems Roadmap 2002, STW]

Importance of Power and Energy Consumption

Current UMTS phones can hardly be operated for more than an hour, if data is being transmitted.[from a report of the Financial Times, Germany, on an analysis by Credit Suisse First Boston; http://www.ftd.de/tm/tk/9580232.html?nv=se]


Key requirements for embedded software

Hardware/software efficiency run-time efficiency, code-size efficiency, energy efficiency, power consumption, .....

Many standards published as „reference implementations“ (just provide the correct results; do not care about efficiency)

proposal of the „software washing machine“ (Catthoor)

„dirty“ unoptimized software in

„clean“ optimized software out


Generating efficient software requireswork at all levels

Algorithmic level(using the most efficient algorithm + data structures)

High-level source code transformations Compiler optimizations Code-Compression Operating system support

(e.g. for minimizing power consumption)


Algorithmic level

Choosing best decoding/filtering etc. algorithm+data structures

Example: MPEG-2 data structures: Inverse Discrete Cosine Transform (IDCT) most power/cycle hungry hot spot.

Transformations: Replacing „double“ by „float“

[still acceptable quality] Energy consumption reduced to 34%,

cycles reduced to 35 % Standard IDCT „Fast IDCT“ („double float“ „integer“),

[significant loss of precision]. Energy consumption reduced to 4.86%,

cycles reduced to 5.10% [T. Huels, Inf 12, UniDo, 2002]


High-level transformations

Example: Separation of margin handling

+

many if-statements for margin-checking

no checking,efficient

only few margin elements to be processed


if (x>=10||y>=14) for (; y<49; y++) for (k=0; k<9; k++) for (l=0; l<9;l++ ) for (i=0; i<4; i++) for (j=0; j<4;j++) { then_block_1; then_block_2}else {y1=4*y; for (k=0; k<9; k++) {x2=x1+k-4; for (l=0; l<9; ) {y2=y1+l-4; for (i=0; i<4; i++) {x3=x1+i; x4=x2+i; for (j=0; j<4;j++) {y3=y1+j; y4=y2+j; if (0 || 35<x3 ||0 || 48<y3) then-block-1; else else-block-1; if (x4<0|| 35<x4||y4<0||48<y4) then_block_2; else else_block_2;}}}}}}

Loop nest splitting at University of DortmundLoop nest from MPEG-4 full search motion estimation

for (z=0; z<20; z++) for (x=0; x<36; x++) {x1=4*x; for (y=0; y<49; y++) {y1=4*y; for (k=0; k<9; k++) {x2=x1+k-4; for (l=0; l<9; ) {y2=y1+l-4; for (i=0; i<4; i++) {x3=x1+i; x4=x2+i; for (j=0; j<4;j++) {y3=y1+j; y4=y2+j; if (x3<0 || 35<x3||y3<0||48<y3) then_block_1; else else_block_1; if (x4<0|| 35<x4||y4<0||48<y4) then_block_2; else else_block_2;}}}}}}

for (z=0; z<20; z++) for (x=0; x<36; x++) {x1=4*x; for (y=0; y<49; y++)

analysis of polyhedral domains, selection with genetic algorithm

[H. Falk et al., Inf 12, UniDo, 2002]


Results for loop nest splitting- Execution times -

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

Cavity Motion Estimation QSDPCM



Results for loop nest splitting- Code sizes -

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

Sun

Pentiu

m HPM

IPS

PowerPC

DEC Alp

ha

TriMed

ia

TI C6x

ARM7

thm

b

ARM7

arm

Averag

e

Cavity Motion Estimation QSDPCM



Compilers: Translation from C critical bottleneck

(Real-time) UML or equiv.(Real-time) UML or equiv.

StateCharts/SDLStateCharts/SDL

(sets of) C-programs(sets of) C-programs

Assembly levelAssembly level

RT-JavaRT-Java

Assembly levelAssembly level

VHDLVHDL

HWHW

(Real-time) UML or equiv.(Real-time) UML or equiv.


Overhead of compilers for DSP processors

DSPStone (Zivojnovic et al.). Example: ADPCMCycle overhead [× n]

DSP56001TI-C51 ADI-2101

1.0

2.0

3.0

4.0

5.0

8.0

7.0

6.0

Optimizations exploiting architectural features of embedded processors.

Current focus: VLIW processors (powerful multimedia processors).

In this talk: focus on energy consumption.


Larger & off-chip memories need more energythan smaller & on-chip memories

0

0.5

1

1.5

2

2.5

64 128 256 512 1024 2048 4096 8192

Memory size

En

erg

y p

er a

cce

ss

[nJ

]

Example (CACTI Model):

[Steinke et al., Inf 12, UniDo, 2002]


Example: Off-chip vs. on-chip memories

ARM7TDMI cores, well-known for low power consumption

ARM Atmel Evaluation Board

Processor

On-chip memory

board

On-board memory


On-chip vs. off-chip current

Current32 Bit-Load Instruction (Thumb)

48,2 50,9 44,4 53,1

11677,2 82,2

1,16

0

50

100

150

200

Prog Off-Chip/Data Off-Chip

Prog Off-Chip/Data On-Chip

Prog On-Chip/Data Off-Chip

Prog On-Chip/Data On-Chip

mA

Core+On-Chip-Memory Current (mA) Off-Chip-Memory Current (mA)

Example: Atmel ARM-Evaluation board

Processor

On-chip memory

board

On-board memory

current reduction:

/ 3.02

current reduction:

/ 3.02


On-chip vs. off-chip energy

Energy32 Bit-Load Instruction (Thumb)

115,8

51,6

76,5

16,4

0,020,040,060,080,0

100,0120,0140,0

Prog Off-Chip/Data Off-Chip

Prog Off-Chip/Data On-Chip

Prog On-Chip/Data Off-Chip

Prog On-Chip/Data On-Chip

10

nJ

Energy

Example: Atmel ARM-Evaluation board

€

Off-chip access takes more cycles savings (86%) are larger than for the current.

energy reduction:/ 7.06

energy reduction:/ 7.06


Exploitation of on-chip memory

Which segment (array, loop, etc.) to be stored in on-chip memory?

Gain gi and size si for each segment i.

Maximise gain G = gi, respecting constraint K si.

Static memory allocation:

Solution: knapsack algorithm.

Dynamic reloading:

Where to insert calls to copy function? IP-model

Processor

On-chip memory,capacity K

board

On-board memory

?

For i .{ }

for j ..{ }

while ...

Repeat

call ...

Array ...

Int ...

Array

Example:


Why not just use a cache ?

0

1

2

3

4

5

6

7

8

9

256 512 1024 2048 4096 8192 16384

memory size

En

erg

y p

er

ac

ce

ss

[n

J]

.

Scratch pad

Cache, 2way, 4GB space

Cache, 2way, 16 MB space

Cache, 2way, 1 MB space

Energy consumption in tags, comparators and muxes significant.

[R. Banakar, S. Steinke, B.-S. Lee, 2001]


Results for optimization algorithm

Energy saving

0,00% 10,00% 20,00% 30,00% 40,00% 50,00%

Be

nch

ma

rk

Onchip/MemSize

Energy Saving

[Steinke et al., Inf 12, UniDo, 2002]

0.5%


Total energy reduction for MPEG-2 [%]

100

33.97

31.83

21.68

6.21

4.87

0 20 40 60 80 100

Original

Algorithm (float)

High-level opt.

Compiler opt.

Cache

Scratch pad (static)

[T. Huels, Inf 12, UniDo, 2002]


Optimization technique for microcontrollers and network processors: Bit-field detection

Assembly:mov b, 1, a, 0, 3 # Cost: 1

a

1

«

|

&

7

&

b 0xF1

b a

[Wagner, Inf 12, UniDo, 2002]

b

=


Results available to industry?

„Center of excellence“ (IMEC)

„Center of excellence“ (IMEC)

Informatik 12, UniDo

Informatik 12, UniDo

Design houses/ semiconductor vendors

Design houses/ semiconductor vendors

ICD e.V.(technology transfer center)

ICD e.V.(technology transfer center)

CAD vendors

CAD vendors

partner‘s of the trinity model

yes!


Code compression/decompression

ROM

µP

decompressor

µP

ROM

Key idea:

Very good survey: Rik van de Wiel: The Code Compaction Bibliography, www.extra. research.philips.com/ccb/

Addr Addr


Variable-voltage/frequency example: INTEL Xscale

Fro

m I

nte

l’s W

eb

Site

OS should schedule distribution of the energy budget.


Conclusion

At the algorithmic level At the level of high-level transformations Within the compiler At the code compression level Within the Embedded OS

Making embedded software efficient requires efforts at alllevels:

The focus of this talk was on compilers and energy efficiency;

using new algorithms, the energy consumption can be significantly reduced..

p. marwedel: embedded software:how to make it efficient? slide -1 - embedded software: how to make...

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