Transcript Some Issues in System-Level Power Optimization Abdil Rashid Mohamed, ESLAB, Ph.D. student System-Level Power Optimization Techniques -1-

Some Issues in System-Level Power
Optimization
Abdil Rashid Mohamed, ESLAB, Ph.D. student
System-Level Power Optimization Techniques
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Presentation Organization
• 1015 - 1115  Abdil
– System-level Power/Energy Optimization Techniques
• 1115 - 1130  coffee break
• 1130 - 1200  Mehdi part I
• 1315 -1345  Mehdi part II
– Dynamic Power Management
– Low Power Software Generation
• 1345 – 1400  coffee break
• 1400 – 1500  Aleksandra
– Low Power/Energy Scheduling for Real–time Systems
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Outline
• Motivation - the compeling need for low power systems
• Power reduction at
– conceptualization and modeling levels
– design level - design of power efficient
• hardware units
• memories and
• communication buses
• Conclusion
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Why Low Power Electronic Systems ?
• Power/Energy is expensive, non-renewable and negatively
impacts on environments
• Extend life of battery powered systems: laptops, PDAs.
• In desktops and servers high power consumption raises
temperature and deteriorates performance and reliability.
• Increases need for cooling mechanisms.
• Technical feasibility of high performance computation due to heat
extraction
• Power efficiency has: economic, ecological and ethical reasons.
• It worth mention: power crisis in California, Tanzania.
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Power Reduction Techniques
• Static techniques for low power
– Applied at conceptualization and design time
– Synthesis for low power
– Compilation for low power
• Dynamic techniques for low power
– Dynamic power management (DPM) - use run time behavior to reduce
power consumption when system is serving light load or when idle
• Dynamic voltage scaling (DVS)- change voltage at run time to manage power
• Shutdown unused I/O devices, NIC, display or HDs
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Hardware Technologies for Low Power
Pdyn  f  V  CL  
2
dd
• Very low supply voltage technologies.
• Multiple supply voltages on a single chip.
• Techniques for handling dynamically variable
supply voltage and/or clock speed.
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System Organization and Sources of power
Consumption
• Main consumers of energy in HW are:
– computation, communication and storage units.
• Does software consume power ?
• Energy efficient design of HW/SW systems:
– Need support of a design flow that takes power consumption
into account at all steps of the design process.
– Power estimation metrics at different abstraction levels
– Drawbacks: Metrics are less accurrate at higher levels
– Power depends on implementation specific details
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Power Consideration at All Levels and Dimensions
Organized in two dimensional taxonomy
Computation -> communication -> storage
Specification and constraints
Power constraints
Abdil
Conceptualization and
modeling
Aleksandra
Algorithms and architecture
Hardware & Software
design
Energy efficient design
of both HW and SW
Hardware platform & software
System management
Mehdi
Hardware, software and
run-time management
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Conceptualization and Modeling:
Specification and Implementation Models
Classes of Systems
General-purpose Systems
Special-purpose Systems
System modeling Styles
Functional models
Implementation models
Executable
Non-executable
Which modeling style is good
for power consideration ?
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Specification and Implementation Models
• Functional models
– addresses functionality and requirements
– executable (VHDL, C++, Java; for simulation ) or non executable ( task
graph )
• Implementation models
– describe the target realization for systems.
– system complexity: modular, component oriented, hierarchical.
– Implementation models for energy efficient systems modelling:
• Spreadsheet model-expresses a combination of components and evaluates
overall energy budget
• Power state machine model captures the power consumption of systems and
their constituents as they evolve through a sequence of operational states.
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Energy Efficient Design from Executable
Functional Models
• Algorithm selection for low power
– For a given common function, make a library of multiple different
algorithms
– Characterize each algorithm with performance & power
– Perform system optimization by:
• heuristic to select an implementation algorithm and supply voltage that
trades off performance for power.
• Algorithm computational energy
– Computational energy of the algorithm can be estimated using CDFG
– Characterize each elementary operation with a computational energy
metric
– Compose rules to compute energy cost of a complex CDFG.
– Energy of elementary operations is obtained by assuming implementation
style and extract cost per operation through experiments
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Energy Efficient Design from Executable
Functional Models
• Algorithm communication and storage energy
–
–
–
–
communication and storage cost is hidden in specifications
storage and communication energy: relate to locality of computation data
data variables with long life time -> increased storage need -> more power
problem: locality analysis from CDFG is hard, information not explicitly
available
• Computational kernels
– is an inner loop of an algorithm where most of the time is spent during
execution
– extract them by profiling data on executable system level model
– implement on dedicated power-optimal hardware
– during execution of kernel, rest of system can be shutdown, hence save power
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Power Estimation for non executable
functional models: Task Graph
T5
0.5
0.1
T2
0.1
0.5
T6
0.5
0.5
0.3
Deadline = 70
T1
Period < 100
0.1
0.3
0.1
T7
0.5
T3
0.5
T4
0.4
0.1
Power
PE1
PE2
Time
PE1
PE2
T1
10
20
T1
5
8
T2
15
30
T2
7
12
T3
10
20
T3
4
7
T4
11
7
T4
8
5
T5
32
41
T5
20
35
T6
7
10
T6
2
3
T7
12
22
T7
11
17
Link
Pow
Speed
Mem
Pow
Speed
L1
10*BW
40*BW
M1
50*S
12*S
L2
20’BW
30*BW
M2
60*S
15*S
PE allocation, binding, scheduling
Mem allocation
Communication
PE1: {T1->T2->T3->T7->T4}
M1: {T5, T6}
PE1 <->PE2: L1
PE2: {T5->T6}
M2: {T1, T2, T3, T4, T7}
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Task Graph (contd. )
– Computation energy:
EPE = EPE1 + EPE2 = (10+15+10+11+12) +(41+10) = 109
– Storage energy:
EM = EM1 + EM2 = (0.5+0.3).50 +(0.1+0.1+0.3+0.1+0.1).60 = 88
– Communication energy:
ECom = EPE1->PE2 + EPE2->PE1 = (0.1+0.4).20 = 10
– Total energy:
E = EPE+EM+Ecom = 109+88+10 = 206
– Heuristic to minimize power for the task graph implementation
– Drawbacks: Need for exhaustive pre-characterization and loss
of accuracy due to lack of information on the effects caused by
hardware sharing.
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Energy efficient design from
implementation models
• Spreadsheet model
– Expresses a combination of components and evaluates overall
energy cost
– Useful when designing systems that use specific parts and
interconnect topologies
– Estimates the impact of a component on the power budget
– Total power is the sum of the power of all components.
– Power consumption of all components is taken from data sheets
and collected in a spreadsheet.
– Drawback: do not model interaction between components
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Energy efficient design from implementation
models
• Power State Machine (PSM)
– State based model for system components
•
•
•
•
states represent modes of operations
arcs represent legal transitions between op. modes
states are labelled with power dissipation values
transitions are labelled with triggering events,
energy costs and transition times.
– Advantages:
• study how system reacts to different workloads
• model interactions between components
• analyze the effects of power management.
P=250mW, activity
RW
Idle(1μsec) RW(10μsec)
P=40 μW
IDLE
off
RW(150μsec)
off
P=0 μW
OFF
PSM for a memory component
– Drawbacks: complex component model
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Low Power Application Specific Units
• Usually gives better power efficiency, but have low flexibility
• Power reduction techniques: low power RTL, logic level and
physical level techniques
– Power Driven Voltage Scaling (PDVS) and scaling down Vdd, reduce
power, but performance may diminish.
– multiple supply voltages on a single chip (globally asynchronous and
locally synchronous systems (GALS) )
– reduce clock frequency, load capacitance and switching activity.
– set clock frequency of a component that is not performing useful work to
zero and nullify dynamic power consumption of that component
• A. Hemani: transformed single clock industrial designs into
GALS - 70% power reduction
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Low Power through Switching Activity Reduction
• Reduce the number of basic operations, -> transform DFG to minimize the
number of operations
• Reduce switching of the inputs to functional unit (FU) -> increase correlation
between successive patterns at the input of FU.
• Scheduling and binding for reduced switching activity
C
A
B
+1
+3
+1 and +3 bound to
the same adder
H
+4
*2
A
B
>5
(a)
Less power efficient schedule
and binding
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A
H
C
+4
+3
*2
>5
(b)
Power efficient schedule &
binding
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Application Specific Processors
• Provides high degree of flexibility, programmability, and reuse
• Not energy efficient and have several disadvantages:
– Power overhead for instruction fetch and decoding
• not an issue for computation units with hardwired control
– Perform computation as a sequence of instruction execution ->power
overhead
• can not take full advantage of algorithmic parallelism.
– Can perform a limited number of elementary operations specified by ISA
• Low power technique: Instruction subsetting -> reducing the
number of instructions supported by ASIP
– Reduce instruction decoding and micro architectural complexity
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Core Processors
• Reduce power by:
– Voltage scaling
• low power version of μPs has low supply voltage
– Dynamic variable voltage supply
– Low power micro architecture design (critical path redesign)
• avoid useless switching activity in idle units
– Special instructions - enhance power & performance
• subword parallel, special addressing mode, multiplyaccumulate
• problem: hard to design compilers for special instructions
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Design of Power-Efficient Memory Subsystems
– Memory accesses are slow and consume more power
with increasing memory size
• reduce memory storage requirements of the applications
• during system conceptualization use principle of temporal
locality to reduce memory storage requirement
• improve locality and reduce need for temporary storage of
results of computation by consuming them ASAP
• Reduce memory need by data compression
– Advanced hierarchical memory architectures for low
power
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Hierarchical Memory Models
Level 3
FU
Level 0
Level 1
Level 2
FU
P0,T0
P1,T1
P2,T2
P3,T3
• Power and access time increases as we move up
memory hierarchy
• Exploit non uniformities in access frequencies of data
– Place frequently accessed locations in low hierarchies to
minimize average cost per access
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Low Power Communication Resources
– At physical level communication power is reduced :
• scaling down the voltage swing on the high capacitance
wires of the bus
• scaling down the average number of signal transitions
– low power data encoding
– Arbitration protocols
Minimum switching
Module A
Enc
bus
Dec
Module A
control
• bus access control
• reduce bus power by scheduling & binding highly
correlated data streams consecutively on the bus
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Low Power Bus Design
- Low power bus design techniques
- lower switching activity, reduce capacitance to be switched
- minimize bus length by module placement and bus routing
- build hierarchical bus
- Bus segmentation –
- transform a long heavily loaded global bus into a partitioned
multistage network by inserting pass transistors on the bus
lines to separate various local buses (segments)
- partitioning can reduce bus power by 60%
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Conclusion
• A balance between power and performance
• Designing energy efficient systems is a
multifaceted problem: high degree of freedom
for power reduction at all abstraction levels
• Main referenece: Benini, D. Micheli, ”System
level Power Optimization: Techniques and
Tools”
THE END
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Power Crisis
Just for your information !
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It worth mention: Power crisis in California, Tanzania.
Companies install their own power plants to cope with the problem.
12$ additional surcharge per day per room due to increased power cost at some hotels.
Refereences from:
http://www.aspstreet.com/archive/d.taf/what,show/id,6362/sid,14 Keeping the Silicon Burning:
California's Power Crisis Concern for Data Centers The lights in California may fade, but data and
Web pages are still served up. “What we’ve done is protect our customers,” asserts Lloyd
Howison, senior manager of construction and engineering for Web hosting at WorldCom. Data
centers themselves contribute significantly to the power problem. Full of servers, disk drives,
networking gear and cooled air, data centers consume a staggering amount of electricity.
Reportedly, a data center gobbles as much power as six office buildings. A widely publicized 1999
study estimated that eight percent of the U.S. consumption of electricity was Internet related.
An Internet service provider data warehouse with 8,000 servers consume as much as 2 MW of
power. A household in Tanzania gets only about 600KWh per month.
NB: “Since embedded systems are increasingly being used and are massively produced not only
for mobile devices, but most of them end up in stationary home based or industry based devices,
every Mw of power that can be saved will result in tremendous power savings in total due to mass
production of similar embedded VLSI systems” by Abdil.
Since energy consumption of electronic systems will scale up as they become more complex and
integrated, energy-efficient electronic system development is mandated by economic, ecological
and ethical reasons.
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