Post on 24-Feb-2016
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Low Power Solutions:A System Design Perspective
Nik Sumikawa
Nik Sumikawa
Contents
Low Power: Why?1
Standard Embedded Solutions2
Innovative Solutions33
Solutions for Mobile Platforms44
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Low Power: Why?Power vs. Performance
Technology Scaling VLSI Embedded
Technology Trend Green Stimulus Scaling Size
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What You Should Think AboutLow power design strategies
Components: Microcontrollers, peripherals, ect.
Low power design with hardware Low power design with software Low power design in mobile device
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Low Power Embedded Systems
TELOS: Low power wireless
embedded system Low duty cycle
principle Minimizes dynamic
power consumption
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Nik Sumikawa
Low Duty Cycle Principle
WakeUp
Process
SleepPrepDeep
Sleep
Sleep Mode
Timer or Interrupt event
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Low Duty CycleLow processing to sleep ratio
Extended sleep period
Responsively: fast wake-up and sleep times
Minimize Interrupts: Context switching overhead
Nik Sumikawa
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Low Duty Cycle: DMADirect Memory Access (DMA):
Controls bus and transfers data with minimal processor overhead
Significance Transfer data while sleeping Minimize processor overhead
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Low Duty CycleFails with significant
processing
Alternatives: Dynamic Voltage and
Frequency Scaling (DVFS)
Dynamic Power Management (DPM)
Nik Sumikawa
Image: http://www.domainmagnate.com/wp-content/uploads/2009/03/failure-success.jpg
Nik Sumikawa
Dynamic Power
DynamicPower
P = CVdd2f
Capacitance
Frequency
Voltage
Energy Source
Battery
Design Variables
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Reducing Dynamic Power
Dynamic Voltage and Frequency Scaling Scale voltage when sleeping/Idle
Voltage term quad. proportional to power Reduce frequency Minimize line capacitance
Long traces have large capacitance
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Dynamic Power Management
Generalize power management
Multiple policies Single-policy Multiple-policy Task-scaling
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Rajami and Brock [2]
Single-policy StrategyIdle Scaling (IS)
Operate at full speed when processing workload
Reduce the frequency and voltage when idleGoal:
Reduce the CPU and bus frequencies Meet continuous DMA requirements Provide acceptable latency when resuming
from idle
Nik Sumikawa
Rajami and Brock [2]
Multi-policy StrategiesLoad scaling (LS):
Balance system operating point with current or predicted processing demands
Run system with minimal idle timeOther:
Manage systems state based on status of the systems energy source
Nik Sumikawa
Rajami and Brock [2]
Task-scaling StrategiesApplication scaling (AS):
Used for workloads that are difficult to power manage• Audio and video processing• Begin processing next sample immediately
Operate a lower operating point Increases to higher operating point when it
begins to fall behind.
Nik Sumikawa
Rajami and Brock [2]
Results of DPM
IS: Idle Scaling LS: Load Scaling AS: Application Scaling
Frame-Scaling (FS): perfect knowledge of processing requirements of video frame
Nik Sumikawa
Rajami and Brock [2]
Too Many Low Power States
Disadvantages: Confusion Wrong low power
state
Solution: Minimize the number
of state Decrease complexity
Nik Sumikawa
Image: http://kunaljanu.files.wordpress.com/2009/02/ ist2_1457667confusion-1.jpg
Sources of Power ConsumptionMicrocontrollerBus architecture
On chip communication External communication
Memory hierarchyPeripherals
Nik Sumikawa
Rajami and Brock [2]
Communication ArchitecturesAdvanced Microcontroller Bus Architecture
ARM bus protocol for system-on-a-chip (SOC) Advanced High Performance Bus (AHB)
• Pipelined• Memory mapped• Up to 16 masters, 16 slaves
Advanced Peripheral Bus (APB)• Non pipelined• Single master, up to 16 peripherals
Nik Sumikawa
Rajami and Brock [2]
AMBA On-chip BusNik Sumikawa
Rajami and Brock [2]
Power ProfilingNik Sumikawa
Rajami and Brock [2]
86% power consumed by logic14% power consumed by bus lines
Power Reduction TechniquesPower Management
Shut down bus interfaces to idle slavesBus Encoding
Reduces # of line transitions, but not bus transactions
Traffic Sequencing Reduce multiple masters interleaving bus
access
Nik Sumikawa
Rajami and Brock [2]
Power Reduction TechniquesNik Sumikawa
Rajami and Brock [2]
No technique achieves large saving alone
Power vs EnergyPower is amount of energy over an
amount of time (Watts = Joules / second)Battery provides finite amount of energy
Goal: minimize energy use, not just powerIn mobile systems we care about energy
Budget energy to prolong battery life
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Rajami and Brock [2]
Static System OptimizationCompiler techniques
Instruction energy consumption profiling• Done empirically
Instruction reordering• Without affecting correctness• Improve register utilization• Reduce memory accesses• Reduce pipeline stalls
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Static System OptimizationCode Compression
Post compilation static optimization Reduces storage size of instructions Can have a large impact Requires complex design space exploration Goal for mobile system: reduce power
consumption while preserving performance
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Code Compression ChallengesRandom access decompression
Defining decodable block beginnings Jump to new locations in program without
decoding all blocks betweenSolutions
Begin compressed blocks on byte boundaries Store translation table
• More efficient the compression, larger the table Recalculate branch offsets to compressed
addresses
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Code Compression RequirementsAdditional hardware
Additional memory to store table Decompression unit
Design decisions Table generation/lookup Compression technique
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Code Compression ImplementationSPARC ISAOptimize consumption of complete SOCMultiple iterations on binaryInstructions split into 4 categories
Group 1: immediate instructions (code = 0) Group 2: branch instructions (code = 11) Group 3: dictionary instructions (code = 100) Group 4: uncompressed instr (code = 101)
Nik Sumikawa
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Diagram
Optimized Binary
CompiledBinary
Update branch offsets
Branch compression
Markov model
Phase 4
Phase 3Immediate compression
Phase 2
Phase 1
As a Result…Bus Compaction
Instructions transmitted no longer require entire bus
Use the extra lines to transmit the next compressed instruction
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Decompression ArchitecturePre-Cache
Decompression engine between memory/cache
Post-Cache Decompression engine between cache/cpu
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SimulationFull SOC simulation7 sample apps run
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ResultsNik Sumikawa
INCLUDE?Nik Sumikawa
ResultsNet energy saving observed
22-82% power savings from code compression
What about additional hardware?Bonus
Increased performance Reduced area
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VerdictStatic power optimization
Potentially large payoff for little preprocessingStill more sources of consumption
We’ve observed SOC savings What about peripherals?
Nik Sumikawa
Nik Sumikawa
Energy Budget
Voice Call
SMS
Emails
Pictures
localization
Energy Budget
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Energy Budget: Localization
How much of the energy budget should be given to localization? Depends on the user
Grant increase allotment when localization is a higher priority
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Localizations Methods
1
GPS• Very accurate• Power
Hungry
2
GSM• Lower accuracy• Lower power
requirement
3
WiFi• Mod. Accurate• Mod. Power
requirement
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Constandache, Gaonkar, Sayler, Choudhury, Cox [3]
Power vs. Precision
Power: amount of energy required by peripheral in order to determine location
Localization
Precision:Accuracy of the device used for localization
Power Consumptionwww.themegallery.com
Constandache, Gaonkar, Sayler, Choudhury, Cox [3]
30 Second sampling intervals
Power Consumption: GPS: High baseline WiFi: Low baseline with high spikes GSM: Low baseline with varying spikes
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Power Consumptionwww.themegallery.com
30 Second sampling intervals
Results: GPS: increased
baseline
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Localization Accuracy
Accuracy varied based on location
ALE: Average Location Error
Wifi and GSM oversampled
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