CS 252 Graduate Computer Architecture
Lecture 14: Embedded Computing
Krste Asanovic
Electrical Engineering and Computer Sciences
University of California, Berkeley
http://www.eecs.berkeley.edu/~krste
http://inst.eecs.berkeley.edu/~cs252
Time (processor cycle)
Recap: Multithreaded Processors
Superscalar
Fine-Grained Coarse-Grained
Thread 1
Thread 2
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Multiprocessing
Thread 3
Thread 4
Simultaneous
Multithreading
Thread 5
Idle slot
2
Embedded Computing
Sensor Nets
QuickT ime ™an d a
TIFF ( Uncomp res sed) deco mpre ssor
ar e need ed to see this pictur e.
Cameras
Set-top
boxes
Games
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
QuickTime™ and a
TIFF (Uncompress ed) dec ompres sor
are needed t o s ee this pic ture.
Media
Players
Printers
Robots
Smart
phones
Routers
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Automobiles
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Aircraft
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What is an Embedded Computer?
 A computer not used to run general-purpose
programs, but instead used as a component of a
larger system. Usually, user does not change the
computer program (except for manufacturer upgrades).
 Example applications:
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Toasters
Cellphone
Digital camera (some have several processors)
Games machines
Set-top boxes (DVD players, personal video recorders, ...)
Televisions
Dishwashers
Car (some have dozens of processors)
Internet router (some have hundreds to thousands of processors)
Cellphone basestation
.... many more
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Early Embedded Computing
Examples
• MIT Whirlwind, 1946-51
– Initially developed for real-time flight
simulator
– IBM later manufactured versions for SAGE
air defence network, last used in 1983
• Intel 4004, 1971
– developed for Busicom 141-PF
printing calculator
– Intel engineers decided that
building a programmable computer
would be simpler and more flexible
than hard-wired digital logic
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Reducing Cost of Transistors Drives
Spread of Embedded Computing
• When individuals could afford a single transistor, the
“killer application” was the transistor radio
• When individuals could afford thousands of
transistors, the killer app was the personal computer
• Now individuals can soon afford thousands of
processors, what will be the killer apps?
In 2007:
• human population growth per day >200,000
• cellphones sold per day
>2,000,000
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What is different about embedded
computers?
• Embedded processors usually optimized to perform
one fixed task with software from system
manufacturer
• General-purpose processors designed to run flexible,
extensible software systems with code from thirdparty suppliers
– applications not known at design time
• Note, many products contain both embedded and
general-purpose processors
– e.g., smartphone has embedded processors for radio baseband
signal processing, and general-purpose processors to run thirdparty software applications
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Lesser emphasis on software
portability in embedded applications
• Embedded systems
– can usually recompile/rewrite source code for different ISA, and/or
use assembler code for new application-specific instructions
– processor pipeline microarchitecture and memory capacity and
hierarchy known to programmer/compiler
– mix of tasks known to writer of each task, usually static: uses
custom run-time system
– each task usually “trusts” others, can run in same address space
• General-purpose systems
– must have standard binary interface for third-party software
– compiler doesn’t know about this particular microarchitecture or
memory capacity or hierarchy (compiled for general model)
– unknown mix of tasks, tasks dynamically added and deleted from
mix: uses general-purpose operating system
– tasks written by various third-parties, mutually distrustful, need
separate address spaces or protection domains
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Embedded application
requirements & constraints
 Real-time performance
 hard real-time: if deadline missed, system has failed (car brakes!)
 soft real-time: missing deadline degrades performance (skipping frames
on DVD playback)
 Real-world I/O with multiple concurrent events
 sensor and actuators require continuous I/O (can’t batch process)
 non-deterministic concurrent interactions with outside world
 Cost
 includes cost of supporting structures, particularly memory
 static code size very important (cost of ROM/RAM)
 often ship millions of copies (worth engineer time to optimize cost down)
 Power
 expensive package and cooling affects cost, system size, weight, noise,
temperature
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What is Performance?
 Latency (or response time, or execution time)
– time to complete one task
 Bandwidth (or throughput)
– tasks completed per unit time
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Performance Measurement
Worst
case
rates
Average rates
Inputs
C
B
A
Processing Rate (Inputs/Second)
Average rate: A > B > C
Worst-case rate: A < B < C
Which is best for desktop performance? _______
Which is best for hard real-time task?
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_______
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Processors for real-time software
• Simpler pipelines and memory hierarchies make it
easier (possible?) to determine the worst-case
execution time (WCET) of a piece of code
– Would like to guarantee task completed by deadline
• Out-of-order execution, caches, prefetching, branch
prediction, make it difficult to determine worst-case
run time
– Have to pad WCET estimates for unlikely but possible cases,
resulting in over-provisioning of processor (wastes resources)
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Power Measurement
I
V
 Energy measured in Joules
 Power is rate of energy consumption
measured in Watts (Joules/second)
 Instantaneous power is Volts * Amps
Battery Capacity Measured in Joules
 720 Joules/gram for Lithium-Ion batteries
 1 instruction on Intel XScale processor takes ~1nJ
 ~1 billion executed instructions weigh ~1mg
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Power versus Energy
Peak A
Peak B
Power
Integrate
power
curve to get
energy
Time
 System A has higher peak power, but lower total energy
 System B has lower peak power, but higher total energy
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Power Impacts on Computer System
• Energy consumed per task determines battery life
– Second order effect is that higher current draws decrease effective battery
energy capacity (higher power also lowers battery life)
• Current draw causes IR drops in power supply voltage
– Requires more power/ground pins to reduce resistance R
– Requires thick&wide on-chip metal wires or dedicated metal layers
• Switching current (dI/dt) causes inductive power supply
voltage bounce  LdI/dt
– Requires more pins/shorter pins to reduce inductance L
– Requires on-chip/on-package decoupling capacitance to help bypass pins
during switching transients
• Power dissipated as heat, higher temps reduce speed and
reliability
– Requires more expensive packaging and cooling systems
– Fan noise
– Laptop/handheld case temperature
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Power Dissipation in CMOS
Short-Circuit
Current
Diode Leakage Current
Gate Leakage
Current
Capacitor
Charging
Current
CL
Subthreshold Leakage Current
Primary Components:
 Capacitor Charging (~85% of active power)
 Energy is 1/2 CV2 per transition
 Short-Circuit Current (~10% of active power)
 When both p and n transistors turn on during signal transition
 Subthreshold Leakage (dominates when inactive)
 Transistors don’t turn off completely, getting worse with technology scaling
 For Intel Pentium-4/Prescott, around 60% of power is leakage
 Optimal setting for lowest total power is when leakage around 30-40%
 Gate Leakage (becoming significant)
 Current leaks through gate of transistor
 Diode Leakage (negligible)
 Parasitic source and drain diodes leak to substrate
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Reducing Switching Power
Power  activity * 1/2 CV2 * frequency
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Reduce activity
Reduce switched capacitance C
Reduce supply voltage V
Reduce frequency
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Reducing Activity
Clock Gating
Global
Clock
– don’t clock flip-flop if not needed
– avoids transitioning downstream logic
– Pentium-4 has hundreds of gated clocks
Enable
Latch (transparent
on clock low)
Gated Local
Clock
D
Q
Bus Encodings
– choose encodings that minimize transitions on average
(e.g., Gray code for address bus)
– compression schemes (move fewer bits)
Remove Glitches
– balance logic paths to avoid glitches during settling
– use monotonic logic (domino)
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Reducing Switched Capacitance
Reduce switched capacitance C
–
–
–
–
Different logic styles (logic, pass transistor, dynamic)
Careful transistor sizing
Tighter layout
Segmented structures
A
B
C
A
B
C
Bus
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Shared bus driven by A
or B when sending
values to C
Insert switch to isolate
bus segment when B
sending to C
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Reducing Frequency
• Doesn’t save energy, just reduces rate at which it is
consumed
– Some saving in battery life from reduction in rate of discharge
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Reducing Supply Voltage
Quadratic savings in energy per transition – BIG effect
• Circuit speed is reduced
• Must lower clock frequency to maintain correctness
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Voltage Scaling for Reduced Energy
• Reducing supply voltage by 0.5 improves energy per
transition to 0.25 of original
• Performance is reduced – need to use slower clock
• Can regain performance with parallel architecture
• Alternatively, can trade surplus performance for lower
energy by reducing supply voltage until “just enough”
performance
Dynamic Voltage Scaling
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Frequency
“Just Enough” Performance
t=0
Run fast then stop
Run slower and just
meet deadline
Time
t=deadline
 Save energy by reducing frequency and voltage to
minimum necessary (usually done in O.S.)
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Voltage Scaling on Transmeta
Crusoe TM5400
Frequency
(MHz)
Relative
Performance
(%)
Voltage
(V)
Relative
Energy
(%)
Relative
Power
(%)
700
100.0
1.65
100.0
100.0
600
85.7
1.60
94.0
80.6
500
71.4
1.50
82.6
59.0
400
57.1
1.40
72.0
41.4
300
42.9
1.25
57.4
24.6
200
28.6
1.10
44.4
12.7
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Chip energy versus frequency
for various supply voltages
5.0
VDD (V)
energy per cycle (nJ)
4.5
2.4
2.3
2.2
2.1
2
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
4.16
4.0
3.80
3.5
3.46
3.14
3.0
2.82
2.54
2.27
2.5
2.0
2.01
1.78
1.56
1.35
1.5
1.0
0.98
1.15
0.5
0.0
0
50
100
150
200
250
300
frequency (MHz)
[ MIT Scale Vector-Thread Processor, TSMC 0.18µm CMOS process, 2006 ]
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Chip energy versus frequency
for various supply voltages
5.0
energy per cycle (nJ)
VDD (V)
2x Reduction in Supply Voltage
4.5
2.4
2.3
2.2
2.1
2
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
4.16
4.0
3.80
3.5
3.46
3.14
3.0
2.82
2.54
2.27
2.5
2.0
2.01
1.78
1.56
1.35
1.5
1.0
0.98
1.15
4x Reduction in Energy
0.5
0.0
0
50
100
150
200
250
300
frequency (MHz)
[ MIT Scale Vector-Thread Processor, TSMC 0.18µm CMOS process, 2006 ]
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Parallel Architectures Reduce Energy
at Constant Throughput
• 8-bit adder/comparator
– 40MHz at 5V, area = 530 km2
– Base power, Pref
• Two parallel interleaved adder/compare units
– 20MHz at 2.9V, area = 1,800 km2 (3.4x)
– Power = 0.36 Pref
• One pipelined adder/compare unit
– 40MHz at 2.9V, area = 690 km2 (1.3x)
– Power = 0.39 Pref
• Pipelined and parallel
– 20MHz at 2.0V, area = 1,961 km2 (3.7x)
– Power = 0.2 Pref
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Chandrakasan et. al. “Low-Power CMOS Digital Design”,
IEEE JSSC 27(4), April 1992
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CS252 Administrivia
• Next project meetings Nov 12, 13, 15
– Should have “interesting” results by then
– Only three weeks left after this to finish project
• Second midterm Tuesday Nov 20 in class
– Focus on multiprocessor/multithreading issues
– We’ll assume you’ll have worked through practice questions
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Embedded memory hierarchies
• Scratchpad RAMs often used instead, or as well as, caches
– RAM has predictable access latency, simplifies execution time analysis for
real-time applications
– RAM has lower energy/access (no tag access or comparison/multiplexing
logic)
– RAM is cheaper than same size cache (no tags or cache logic)
• Typically no memory protection or translation
– Code uses physical addresses
• Often embedded processors will not have direct access to offchip memory (only on-chip RAM)
• Often no disk or secondary storage (but printers, iPods, digital
cameras, sometimes have hard drives)
– No swapping or demand-paged virtual memory
– Often, flash EEPROM storage of application code, copied to system
RAM/DRAM at boot
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Reconfigurable lockable caches
• Many embedded systems allow cache lines to be
locked in cache to provide RAM-like predictable
access
• Lock by set
– E.g., in an 8KB direct-mapped cache with 32B lines (213/25=28=256
sets), lock half the sets, leaving a 4KB cache with 128 sets
– Have to flush entire cache before changing locking by set
• Lock by way
– E.g., in a 2-way cache, lock one way so it is never evicted
– Can quickly change amount of cache that is locked (doesn’t
change cache index function)
• Can be used in both instruction and data caches
– Lock instructions for interrupt handlers
– Lock data used by handlers
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Code Size
 Cost of memory big factor in cost of many embedded
systems
 RISC core about same size as 16KB of SRAM
32KB
32KB
Techniques to reduce code size:
 Variable length and complex
instructions
 Compressed Instructions
 Compressed in memory then
uncompressed in cache
 compressed in cache
Intel Xscale (2001)
16.8mm2 in 180nm
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Embedded Processor Architectures
• Wide variety of embedded architectures, but mostly
based on combinations of techniques originally
pioneered in supercomputers
– VLIW instruction issue
– SIMD/vector instructions
– Multithreading
• VLIW more popular here than in general-purpose
computing
– Binary code compatibility not as important, recompile for new
configuration OK
– Memory latencies are more predictable in embedded system hence
more amenable to static scheduling
– Lower cost and power compared to out-of-order ILP core.
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System-on-a-Chip Environment
• Often, a single chip will contain multiple embedded
cores with multiple private or shared memory banks,
and multiple hardware accelerators for applicationspecific tasks
– Multiple dedicated memory banks provide high bandwidth,
predictable access latency
– Hardware accelerators can be ~100x higher performance and/or
lower power than software for certain tasks
• Off-chip I/O ports have autonomous data movement
engines to move data in and out of on-chip memory
banks
• Complex on-chip interconnect to connect cores,
RAMs, accelerators, and I/O ports together
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Block Diagram of Cellphone SoC
(TI OMAP 2420)
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“Classic” DSP Processors
AReg 7
AReg 1
AReg 0
X Mem
Y Mem
Off-chip
memory
Addr X
Addr Y
Multiply
ALU
Single 32-bit DSP instruction:
AccA += (AR1++)*(AR2++)
Acc. A
Acc. B
Equivalent to one multiply, three
adds, and two loads in RISC ISA!
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TI C6x VLIW DSP
VLIW fetch of up to 8
operations/instruction
Dual symmetric
ALU/Register clusters
(each 4-issue)
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
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TI C6x regfile/ALU datapath clusters
32b/40b
arithmetic
32b arithmetic,
32b/40b shifts
16b x 16b
multiplies
32b arithmetic,
address generation
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
11/1/2007
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Intel IXP Network Processors
RISC
Control
Network
10Gb/s
DRAM0
DRAM1
DRAM2
Processor
Buffer RAM
Buffer RAM
Buffer RAM
Buffer RAM
SRAM0
Buffer RAM
SRAM1
Buffer RAM
SRAM2
SRAM3
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Buffer RAM
MicroEngine 15
MicroEngine 1 Microcode
MicroEngine 0
RAM
Microcode
Microcode
RAM
PC0 RAM
Register
PC1
File
PC0
Register
PC0
PC1
Register
File
PC7
PC1
File
Eight
threads
ALU
PC7
per
PC7threads
Eight
ALU
microengine
Eight threads
ALU
per
Scratchpad
per
microengine
Data RAM
microengine
Scratchpad
Scratchpad
Data RAM
Data RAM
16 Multithreaded microengines38
Programming Embedded
Computers
• Embedded applications usually involve many concurrent processes and
handle multiple concurrent I/O streams
• Microcontrollers, DSPs, network processors, media processors usually
have complex, non-orthogonal instruction sets with specialized
instructions and special memory structures
–
–
–
–
poor compiled code quality (% peak with compiled code)
high static code efficiency
high MIPS/$ and MIPS/W
usually assembly-coded in critical routines
• Worth one engineer year in code development to save $1 on system
that will ship 1,000,000 units
• Assembly coding easier than ASIC chip design
• But much room for improvement…
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Discussion: Memory consistency
models
• Discussion: Memory consistency models
– Tutorial on consistency models + Mark Hill’s position paper
– Conflict between simpler memory models and simpler/faster
hardware
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