High-level System Modeling and Power Management Techniques

Jinfeng Liu

Dept. of ECE, UC Irvine

Sep. 2000

Background X2000 Avionics System Architecture

COTS – based building blocks for system integration Low cost component with strong commercial supportWidely accepted specification, design, application and

testingReduced development cost

Dual system bus architectureIEEE 1394 bus

Hi performance on fast data rate Moderate power Reconfigurable structure

I2C bus Low power Adequate data rate for low-speed communication

Power Aware vs. Low Power Low power design – as low as possible

Minimize power consumption at circuit/gate level No system-level and application specific knowledge Limited reconfiguration space to meet multiple mission

requirement Power aware computation – use power wisely

Power model built on application-specific knowledge Reconfigurable system architecture to meet multiple mission

requirement Adaptive adjustment to run-time power supply Optimize power usage on system level

Manage all power consumers – electronics, mechanics, thermal

Regulate power surge to protect batteryShorten execution time to save energy

Examples – Mars Rover Power supply

Non-rechargeable battery and solar panel

Power consumption Electronics – computation, imaging, communication, control Mechanic – driving, steering Thermal – motors must be heated in low-temperature

environment

Power management Low-power electronics cannot make significant power saving No system-level management tool available Manual schedule must remain conservative

Serialize all operations to suppress power surgeLong execution timeSolar power not efficiently used

Our Approach High-level system modeling techniques

Describe the system in high-level abstractions Employ application specific knowledge in system models Apply power aware management techniques on different

power consumers – electronics, mechanics, thermal System modeling

Behavioral modeling – software architecture, application specific knowledge

Architectural modeling – hardware platform built on top of parameterized components

Partitioning – mapping behavioral objects to architectural structures

Scheduling – a valid sequence of concurrent/parallel operations on multiple processors that satisfies real-time requirement

Our Approach Power management and optimization

Behavioral modelingExtract power related attributes of all objects

Architecture modelingUse low-power devices or devices that can operate on

low-power mode Partitioning

Migration – merge computations on under-utilized processors on one processor to improve utilization

Segmentation – separate tightly coupled computations into clusters to localize communication

Scheduling Arrange operation sequences on multi-processor /

multiple power consumer to meet both performance and power requirement

Behavioral Model Application specific knowledge

Input, output and function Dependency and precedence Control and data flow Timing and sequence

Software architecture Operating system features – real-time, centralized,

distributed, and etc. Execution model – event driven, interrupt, distributed agent,

client-server, and etc. Communication model – protocol stack and specification

Power related attributes Data rate, execution time, CPU speed, memory size,

communication path, and etc.

Architectural Model Component – parameterized COTS

Type – processor, memory, I/O, DSP, bus, and etc. Interface – how the components can be connected to each

other Modes – operation modes parameters, voltage, clock speed,

bandwidth, power consumption, and etc.

Package – a bundle of connected components that performs certain operation Components – a set of connected components Internal/external interface – how components are connected Modes – configuration space of the collected components

specified by each component’s working mode and collective attributes, e.g., voltage, speed, power and etc.

Partitioning Mapping – map behavioral objects to hardware

Group related OS, communication, control and application objects into processing nodes

Extract data objects into storage nodes Allocate components/packages for each processing node Arrange data storage for data nodes and optimize storage location

to reduce communication Establish communication paths among nodes that comply with the

communication model Setup working mode of each component/package to fit the

behavioral requirement Extract attribute of each structure

Function – computation, control, communicationCPU utilizationBus trafficPower consumption

Partitioning Migration – combine multiple nodes to one node to improve

utilization Examine the utilization of each node Migrate computation on under-utilized processing nodes and

merge corresponding storage nodes if necessary Balance power consumption and CPU utilization

Segmentation – arrange nodes in tight communication in a bus segmentation Group nodes by communication localities Settle each group in a bus segment (a feature of IEEE 1394) Extract attributes of localized communication mode in a segmented

bus Improved performanceReduced bus trafficReduced power consumption

Scheduling – Techniques Deadline based real-time scheduling on

multiprocessors

Rate-monotonic scheduling – extend existing RM scheduling to multiprocessors

Timing constraint graph scheduling – multiple serializable sequences in a single heart beat

Scheduling – Techniques Constraint logic solving

Transfer all constraints into a pure mathematical form Use tools to solve the problem in mathematical domain Example – CLPR

Constraints C1 > 3, C1 < 5, C2 > 2, C2 < 4 # two power consumers C1 + C2 < S, S > 6, S < 12 # one power source

Inputs C1 = 4.5, S = 7

Results C2 < 2.5 2 < C2

Scheduling – Objectives Our power-aware scheduling tool

A novel graphical tool that visualizes timing and power constraint and transforms them into graph problems

Manage all power sources and power consumers in system-level

Power-aware scheduling – schedule operations based on power source output

Automated schedule to meet both performance requirement and power constraint

Regulate power surge Use power efficiently to reduce execution time Management and optimization tool to give designers a vision

to the power surge at run-time

Scheduling Tool Extended Gantt-chart in real-time scheduling for single processor

Event – binsTiming – horizontal sizePower – vertical sizeEnergy – area of the bin

Power surge – compacting bins downward

Power

TimeStarting time Ending time

Power level Energy consumption

Demo

Scheduling Tool Scheduling chart for multi-processor and multiple power

consumers Events can overlap vertically

Multi-processorMultiple power consumer – electronics, mechanic, thermal

Power awareness – min and max power supply

Demo

A

B B B B

C C C C C

D D D

Constant task A

Periodic task B

Periodic task C

Task D follows B

Power

Time

Scheduling Tool Timing constraints – bin packing problem to satisfy horizontal

constraints Independent tasks – moving bins horizontally Dependent tasks – moving grouped bins horizontally Power/voltage/clock scaling – extending/squeezing bins

Demo

A

B

C

DPower

Time

B

C

Deadline of B (scheduling space) Deadline of B

Min timing constraint of D

Max timing constraint of D

Deadline of C (scheduling space)

Deadline of C

Scheduling space of D

Slide bin within timing space

Squeeze/extend bin to available time slot

C

C

Scheduling Tool Power constraints – bin packing problem to satisfy vertical

constraints Automatic optimization – let tool do everything Manual optimization – visualizing power in manual scheduling

Demo

A

B

C DPower

Time

B

Manual scheduling while monitoring power surge C

A

B

C

D

Power

Time

B

Attack spike

Automated global scheduling to meet min-max power

CC

Max

Min

Improve utilization

Example – Mars Rover System specification

Six wheel motors Four steering motors System health check Hazard detection

Timing constraints System health check

10s/10min Heating motor for 5s,

100s prior to driving Hazard detection 10s –

steering 5s – driving 10s

Example – Mars Rover Power constraints

Solar panel: 14.9W peak power @ noon, 11W for 6hr/sol Battery: 10W max power output. 150W-hr energy storage CPU: 3.7W, constant for 4h/sol Health check: 6.3W, 10s Hazard detection: 7.3W, 10s Heating: 7.5W (1 motor) or 11.3W (2 motors), 5s Steering: 6.8W, 5s (7º/s) Driving: 12.4W, 10s (7cm)

Existing solution Serialize each operation to satisfy power constraint Conservative – longer execution time and under utilization of

solar power No scheduling tool is used

Scheduling Method Constraint graph construction

Nodes: operations Edges: precedence relationship between operations

Channel specification Channels: resources that can perform operations

independently Six wheels heating channels, four steer motor heating channels One driving channel, one steering channel One computation channel

Operations on one channel must be serialized Scheduling

Primary channel selection Schedule primary channel by applying graph algorithms Auxiliary channels and power requirement are considered as

scheduling constraints

Constraint Graph

System health check /

Thc

System health check /

Thc

thc -(thc + Thc)

Heat wheel 1 / Thw

Heat wheel 2 / Thw

Heat wheel 3 / Thw

Heat wheel 4 / Thw

Heat wheel 5 / Thw

Heat wheel 6 / Thw

Heat steer 2 / Ths

Heat steer 3 / Ths

Heat steer 4 / Ths

Hazard detection / Thd

Steer / Ts

Drive / Td

- thw

-ths

Heat steer 1 / Ths

Channel Specification

Hazard detection (C) /

Thc / Phc_CHealth check (C) /

Thc / Phc_C

Heat steer i (C) / Ths_C /

Phs_C

Heat steer i

(T) / Ths_T /

Phs_T

Heat wheel i

(C) / Thw_C

/ Phw_CHeat

wheel i (T) / Thw_T /

Phw_T

Steer (C) / Ts_C /

Ps_CSteer

(M) / Ts_M /

Ps_M

Drive (C) / Td_C /

Pd_C

Drive (M) / Td_M /

Pd_M

-ths + Ths_E

-thw + Thw_E

Health check (C) /

Thc / Phc_C

Computation

Mechanic

Thermal

thc -(thc + Thc)

Scheduling

Hazard detection (C) /

Thc / Phc_C

Heat steer i (C) / Ths_E /

Phs_E

Heat steer i

(T) / Ths_T /

Phs_T

Heat wheel i

(C) / Thw_E

/ Phw_E

Heat wheel i

(T) / Thw_T /

Phw_T

Steer (C) / Ts_C /

Ps_CSteer

(M) / Ts_M /

Ps_M

Drive (C) / Td_C /

Pd_CDrive

(M) / Td_M /

Pd_M

-ths + Ths_E

-thw

Primary channel: Computation

Auxiliary channel: Mechanic

Auxiliary channel: Thermal

Health check (C) /

Thc / Phc_C

thc -(thc + Thc)

-ths

-thw + Thw_E

-Ts_C + Ts_M

Existing Results

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 1600

5

10

15

20

CPU Health check

Heat steer

Heat wheel

Hazard detection

steer Drive

JPL solution Over constraint – serialize every operation to satisfy power

constraint Conservative – longer execution time and under-utilization of solar

power No scheduling tool is used – manual scheduling Not power-aware – scheduling without considering solar power

output

Power

Time

Our Solution

CPU Health check

Heat steer

Heat wheel

Hazard detection

steer Drive

5

10

15

20

Power

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 1600

Time

Power-aware scheduling – high solar power Max solar power output – 14W at noon Relaxed constraint – heating motors while doing other operations Aggressive – do as much as possible

Fastest moving speed – no waiting on heating Improved utilization of solar power

Automated scheduling – use scheduling tools

Our Solution

CPU Health check

Heat steer

Heat wheel

Hazard detection

steer Drive

5

10

15

20

Power

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 1600

Time

Power-aware scheduling – typical solar power Typical solar power output – 11W for 6hr/sol Relaxed constraint –heating motors while doing other operations Moderately aggressive – avoid exceeding power limit

Faster moving speed – some waiting time on heating Improved utilization of solar power

Automated scheduling – use scheduling tools

Our Solution Power-aware scheduling – low solar power

Typical solar power output – 8W at operation threshold Restricted constraint – serialize operations Conservative – save as JPL solution

Slow moving speedFull utilization of low solar power

Automated scheduling – use scheduling tools

CPU Health check

Heat steer

Heat wheel

Hazard detection

steer Drive

5

10

15

20

Power

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 1600Time

Comparison

Solar power output Battery energy Solar energy % of solar energy Moving distance14 134 2010 90% 4 steps - 28cm11 404 1740 99% 4 steps - 28cm8 864 1280 100% 4 steps - 28cm

Solar power output Battery energy Solar energy % of solar energy Moving distance14 388.5 2097.5 94% 6 steps - 42cm11 869.5 1755 100% 5 steps - 35cm8 864 1280 100% 4 steps - 28cm

Existing solution Conservative – long execution time, low resource utilization Not power aware – same schedule for all conditions Not intend to use battery energy

Our solution Adaptive – speedup when power supply is high Power-aware – adaptive scheduling on different power supply Use battery energy when needed