2. Lecture
outline
• Course
informa<on
– Examina<on:
project
• Modern
embedded
systems
– Embedded
systems
design
– Performance
vs.
predictability
• Examples
– Automo<ve
electronics
– PlaySta<on
3,
mobile
phones
– Mars
Pathfinder
Lecture
1/2
3. Course
informa<on
• Team
(see
contact
info
on
CampusNet)
– Jan
Madsen,
course
leader
– Paul
Pop
and
Sven
Karlsson,
lecturers
– Domi<an
(Domi)
Tamas-‐Selicean,
Wajid
Hassan
Minhass
teaching
assistants
• Webpage
– CampusNet
Lecture
1/3
4. Course
informa<on,
cont.
Literature
• Textbook:
Scheduling
in
Real-‐Time
Systems
(full
text
available
online)
• Selected
chapters
(full
text
available
on
CampusNet)
Lecture
1/4
5. Course
informa<on,
cont.
• Lectures
– Language:
English
– 13
lectures
• Lecture
notes:
available
on
CampusNet
as
a
PDF
file
the
day
before
• Examina<on:
project,
see
CampusNet/Project
– Report
evalua<on
and
oral
exam
• 7.5
ECTS
points
Lecture
1/5
6. What
is
an
embedded
system?
• Defini<on
– an
embedded
system
special-‐purpose
computer
system,
part
of
a
larger
system
which
it
controls.
• Notes
– A
computer
is
used
in
such
devices
primarily
as
a
means
to
simplify
the
system
design
and
to
provide
flexibility.
– Oaen
the
user
of
the
device
is
not
even
aware
that
a
computer
is
present.
Lecture
1/6
17. Embedded
systems
are
everywhere
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
18. Characteris<cs
of
embedded
systems
• Single-‐func<oned
– Dedicated
to
perform
a
single
func<on
• Complex
func<onality
– Oaen
have
to
run
sophis<cated
algorithms
or
mul<ple
algorithms.
• Cell
phone,
laser
printer.
• Tightly-‐constrained
– Low
cost,
low
power,
small,
fast,
etc.
• Reac<ve
and
real-‐<me
– Con<nually
reacts
to
changes
in
the
system’s
environment
– Must
compute
certain
results
in
real-‐<me
without
delay
• Safety-‐cri<cal
– Must
not
endanger
human
life
and
the
environment
Lecture
1/18
19. Some
sta<s<cs
• More
than
humans
on
the
planet,
already
– 40
billion
of
such
devices
by
2020
• 99%
of
the
processors
are
used
in
embedded
systems
– 4
billion
embedded
processors
were
sold
last
year
alone
• €71
billion
global
market
in
2009,
growth
rates
of
14%
– Market
size
is
about
100
<mes
the
desktop
market
• Share
of
costs:
– automo<ve
(36%),
industrial
automa<on
(22%),
telecommunica<ons
(37%),
consumer
electronics
(41%)
and
health/medical
equipment
(33%)
• Half
a
million
more
engineers
needed,
worldwide
– expected
to
double
over
the
next
6
years
Lecture
1/19
20. Example
area:
automo<ve
Embedded
systems:
90%
future
innova<ons
ACC
Stop&Go
BFD
40%
price
ALC
KSG
42
voltage
Internet
Portal
GPRS,
UMTS
Telema<cs
Naviga<on
System
Online
Services
CD-‐Changer
BlueTooth
Level
of
dependency
ACC
Adap<ve
Cruise
Car
Office
Control
Local
Hazard
Warning
Airbags
Integrated
Safety
System
DSC
Dynamic
Stability
Steer/Brake-‐By-‐Wire
Control
I-‐Drive
Electronic
Gear
Control
Adap<ve
Gear
Control
Lane
Keeping
Assist.
Electronic
Air
Condi<on
Xenon
Light
Personaliza<on
ASC
An<
Slip
Control
BMW
Assist
Soaware
Update
ABS
RDS/TMC
Force
Feedback
Pedal
Electronic
Injec<ons
…
Telephone
Speech
Recogni<on
source:
BMW
Check
Control
Seat
Hea<ng
Control
Emergency
Call
Speed
Control
…
Autom.
Mirror
Dimming
Central
Locking
…
…
1970
1980
1990
2000
24. Block
Diagram
of
State-‐of-‐the-‐art
Smartphone
Bauery
RF
Baseband
ASIC
Charger
Energy
management
64MB
ASIC
NOR
FLASH
White
LED
ARM9
driver
64MB
Back-‐light
SDRAM
UMA
core
Mixed-‐ LEDs
Signal
ASIC
SIM
2MPix
camera
module
IHF
BT
LED
Flash
Module
Keyboard
Applica<on
512MB
processor
MM
NAND
C
FLASH
ARM9
512
MB
DDR
Frame
DRAM
UMA
core
buffer
ASIC
Posi<on
sensors
LCDs
25. Tradi<onal
embedded
soaware
development
• Design
and
build
the
target
hardware
• Develop
the
soaware
independently
• Integrate
them
and
hope
it
works
Does not work for complex projects
26. System-‐level
design
(Y-‐chart)
Application System platform
model model
Applica<on
model
System-level Architecture
model
design tasks
Model of system
Analysis
implementation
Software Hardware
synthesis synthesis
27. Graphical
illustra<on
of
Moore’s
law
1981 1984 1987 1990 1993 1996 1999 2002
10,000 150,000,000
transistors transistors
Leading edge Leading edge
chip in 1981 chip in 2002
• Something
that
doubles
frequently
grows
more
quickly
than
most
people
realize!
– A
2002
chip
could
hold
about
15,000
1981
chips
inside
itself
29. Design
challenge
–
op<mizing
design
metrics
• Common
metrics
– Performance:
the
execu<on
<me
or
throughput
of
the
system
– Unit
cost:
the
monetary
cost
of
manufacturing
each
copy
of
the
system,
excluding
NRE
cost
– NRE
cost
(Non-‐Recurring
Engineering
cost):
The
one-‐<me
monetary
cost
of
designing
the
system
– Size:
the
physical
space
required
by
the
system
– Power:
the
amount
of
power
consumed
by
the
system
– Flexibility:
the
ability
to
change
the
func<onality
of
the
system
without
incurring
heavy
NRE
cost
30. Design
challenge
–
op<mizing
design
metrics
• Common
metrics
(con<nued)
– Time-‐to-‐prototype:
the
<me
needed
to
build
a
working
version
of
the
system
– Time-‐to-‐market:
the
<me
required
to
develop
a
system
to
the
point
that
it
can
be
released
and
sold
to
customers
– Maintainability:
the
ability
to
modify
the
system
aaer
its
ini<al
release
– Correctness,
safety,
many
more
31. The
performance
design
metric
• Widely-‐used
measure
of
system,
widely-‐abused
– Clock
frequency,
instruc<ons
per
second
–
not
good
measures
• Latency
(response
<me)
– Time
between
task
start
and
end
– e.g.,
Digital
cameras
A
and
B
process
images
in
0.25
seconds
• Throughput
– Tasks
per
second,
e.g.
Camera
A
processes
4
images
per
second
– Throughput
can
be
more
than
latency
seems
to
imply
due
to
concurrency,
e.g.
Camera
B
may
process
8
images
per
second
(by
capturing
a
new
image
while
previous
image
is
being
stored).
• Speedup
of
B
over
S
=
B’s
performance
/
A’s
performance
– Throughput
speedup
=
8/4
=
2
32. Time-‐to-‐market:
a
demanding
design
metric
• Time
required
to
develop
a
product
to
the
point
it
can
be
sold
to
customers
• Market
window
Revenues ($)
– Period
during
which
the
product
would
have
highest
sales
• Average
<me-‐to-‐market
Time (months) constraint
is
about
8
months
• Delays
can
be
costly
33. Power
design
metric:
trends
Nuclear
Reactor
PenHum
4
(Presco[)
PenHum
4
Hot
Plate
PenHum
3
PenHum
2
PenHum
Pro
PenHum
486
386
34. Project:
Why?
• The
course
will
focus
on
the
analysis,
simulaHon
and
design
of
embedded
systems.
• Goal
– Understand
and
apply
simula<on/analysis/design
techniques
for
embedded
applica<ons.
– How:
implement
a
soaware
tool
that
can
simulate/
analyze/design
an
embedded
system.
• Details
– see
these
slides
and
“project.pdf”
on
CampusNet/Project
Lecture
1/34
35. Project:
What?
• Soaware
tool:
– Implementa<on
of
simula<on,
analysis
and/or
design
techniques
for
embedded
systems
A1:
Very
Simple
Simulator
Simulate
the
running
of
an
embedded
applica<on
on
a
single
processor
system,
using
preemp<ve
fixed-‐
priority
scheduling
A2:
Response-‐Time
Analysis
Determine
the
worst-‐case
response
<mes
for
the
embedded
system
simulated
in
A1
Lecture
1/35
36. Project:
How?
• Soaware
tool
– Input
• Models
for
the
applica<on
and
hardware
architecture
(Lecture
2)
– Worst-‐case
execu<on
<me
of
each
process
(Lectures
3)
– Timing
constraints
(deadlines)
• Scheduling
policy
– Fixed-‐priority
preemp<ve
scheduling
(Lectures
4−6)
– Another
scheduling
policy
(Lectures
7−12
and
bibliography)
– Output
• Is
the
applica<on
mee<ng
all
the
deadlines?
– Performance
numbers:
e.g.,
worst-‐case
response
<mes
Lecture
1/36
37. Project:
Deliverables
• Source
code
with
comments
– Programming
language:
any
language
is
fine
• Sugges<on:
Java,
using
the
Jung
graph
library
and
GraphML
for
capturing
the
models:
hup://jung.sourceforge.net/
• See
the
examples
on
CampusNet/Project
• Report
– Document
the
design
and
implementa<on
– Describe
the
results
obtained
– See
the
documents
on
CampusNet/Project
• “soaware_development_projects.pdf”
and
“Systema<c
Soaware
Test.pdf”
Lecture
1/37
38. Project,
cont.
• Milestones
– Early
September:
Group
registra<on
– Late
October:
Advanced
topic
selec<on
• Decide
on
the
“advanced
technique”
to
be
implemented
– Present
your
advanced
topic
and
get
feedback
– End
of
October:
Project
report
draa
• Upload
draa
to
CampusNet
– It
should
contain
a
descrip<on
of
the
advanced
topic
– Beginning
of
December:
Final
report
submission
• Upload
final
report
to
CampusNet
Lecture
1/38
39. Preliminary
lecture
plan
L1
Introduc<on
(Paul
Pop,
Sven
Karlsson)
L2
Performance
analysis
(Jan
Madsen)
Lab:
SimpleScalar
L3
Worst-‐case
execu<on
<me
analysis
(Jan
Madsen)
Lab:
aIT
L4
Scheduling
for
embedded
systems
(Paul
Pop)
Lab:
Project
L5
Schedulability
analysis
(I)
(Paul
Pop)
Lab:
TIMES
L6
Schedulability
analysis
(II)
(Paul
Pop)
Lab:
TIMES
L7
Handling
shared
resources
(Paul
Pop)
Realis<c
exercise
L8
Handling
dependencies
(Paul
Pop)
Lab:
MAST
L9
Parallel
programming
(Sven
Karlsson)
Lab:
OpenMP
L10
Aspects
of
parallel
programming
(Sven
Karlsson)
Lab:
CELL
simul.
L11
Hybrid
scheduling
(Paul
Pop)
Exercise
L12
Mul<-‐core
systems
(Paul
Pop/Jan
Madsen)
Lab:
MAST
L13
Outlook
