Yi Xiang · 2015. 9. 9. · energy harvesting-aware multicore embedded system platforms • Solar energy harvesting as the only power source • Batteries/supercapacitors used for

Yi Xiang

Committee members:

Dr. Sudeep Pasricha (Academic Advisor)

Dr. Anura Jayasumana

Dr. H. J. Siegel

Dr. Michelle Strout

OUTLINE

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Introduction and Preview of Contributions

Contribution I:Semi-Dynamic Scheduling for Independent Tasks: Hybrid Energy Storage, Process Variation, and Thermal Management

Contribution II:Template-Based Scheduling for Task Graphs:Slack Reclamation, Soft Errors, and Hard Failures

Contribution III:Mixed-Criticality Scheduling on Heterogeneous Cores:Soft Deadline, Near-Threshold/Super-Threshold Computing

Conclusion Ou

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• We have been doing this for a very long time:

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What is Energy Harvesting?

wind + mill

windmill

water + wheelwaterwheel

solar + core

solarcore?

Chao Li et al., “SolarCore: Solar Energy Driven Multi-Core Architecture Power

Management”, IEEE International Symposium on High Performance Computer

Architecture (HPCA), pp. 205-216, 2011.

• Collect energy from ambient sources

• Including solar, radio frequency,

magnetic, vibration, thermoelectric, etc.

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Energy Harvesting for Electronic Systems

• To support energy autonomy for electronic devices

• Wearable electronics, wireless sensor networks, etc.

• Limited energy availability for electronic devices:

• Electricity grid is not available everywhere

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Application of Energy Harvesting: Project Loon

Project Loon

o Global network-on-balloon

o Provide internet access for rural

and remote areas

o Float in atmosphere

o Powered by solar energy

harvesting

For many applications energy is not readily available,

motivating the use of energy harvesting

• Energy constraints for embedded devices:

• Limited energy capacity of batteries

• Replacing battery can be inconvenient, costly, or even impractical

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Pervasive computing:

o Billions of sensors

o Scattered everywhere

o Batteries:

o costly or impossible

maintenance

o toxic to environment

o Need energy harvesting to

achieve energy autonomy

User Demands:

High performance

Large screen size

High resolution

GPS

Camera

Biometric sensors

24X7 battery life?External

Battery

Pack

Energy Harvesting and Batteries

• Solar energy harvesting as power supply

• Photovoltaic (PV) panel to scavenge energy from solar radiation

• Why choose solar energy harvesting?

Solar Energy Harvesting

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• Advantages of solar energy harvesting as power supply

• High power density

• Varied scales of PV panels and systems

Why Solar Energy Harvesting?

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Energy Source Typical Power Density

thermal gradient 60 μW/cm2

vibration 4 μW/cm3

radio frequency 1 μW/cm2

solar radiation 100 mW/cm2

• Solar radiation can vary dramatically with environment change

• Energy shortage at times

• Hard to predict

As a result, hard to find a performance- and energy-optimal schedule

for workload running on energy harvesting-aware embedded systems

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Harvesting power trace of an PV array in one dayProvided by National Renewable Energy Laboratory (NREL), Golden, Colorado

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Challenges with Solar Energy Harvesting for

Embedded Systems

Design a holistic framework for performance- and energy-optimal

scheduling and allocation of workload (task, communication) on

energy harvesting-aware multicore embedded system platforms

• Solar energy harvesting as the only power source

• Batteries/supercapacitors used for temporal energy storage

• Multi-core processors with frequency scaling capability

• Real-time periodic workloads with deadline constraints

Focus of this Dissertation

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Real-Time Workload with Different Timing Constraints

• Hard deadline constraint

• Any task miss → total system failure

• Firm deadline constraint:

• Every task miss → inevitable performance penalty

• Soft deadline constraint:

• Each task miss → possible performance penalty

Main objective:

Minimizing miss rate/penalty for task set with firm/soft deadlines

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Overview of Proposed Framework

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• task-to-core mapping

• intra-core scheduling

• communication mapping

• voltage-frequency selection

• dynamic power management

Semi-Dynamic

Workload and Platform

Management Framework

Real-Time Workloads

independent tasks

task graphs

Multicore Platforms

homogeneous

Constraints

timing

temperature

Energy Harvesting Systems

photovoltaic panels

batteries supercapacitors

heterogeneous

core variation

energy

soft error hard error

firm deadline soft deadline

hybrid

Objective

minimize miss rate/penalty

off tE off

tF

offtD

tBtC

tA

multithreaded tasks

OUTLINE

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Conclusion Ou

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Contribution I:

Semi-Dynamic Scheduling for Independent Tasks

• Main Objective

• To reduce miss rate of independent tasks under varying and

stringent energy harvesting conditions

• Contributions

• A semi-dynamic algorithm (SDA) that results in lower task miss rates

compared to best known prior work

• Efficient utilization of multicore systems

• Management of battery/supercapacitor hybrid storage system

• Awareness of discrete frequency levels, process variations, thermal

issues

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Workload: Periodic Independent Task Set

• Multiple independent periodic tasks with firm deadlines

• A task miss: missing deadline of a task instance

• Minimize miss rate → utilize energy as efficient as possible

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An example of

periodic task set

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• Task utilization = execution time at fmax

task period

• fopt = fmax×U

• The lowest frequency that is sufficient to meet all task deadlines,

with Earliest Deadline First (EDF) scheduling

• For energy efficiency: minimize frequency fluctuations

• Main drawback: dynamic task dropping and slowing down on

energy shortage

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Related Work: Utilization-Based Algorithm (UTB)

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J. Lu et al., “Scheduling and Mapping of Periodic Tasks on Multi-Core Embedded

Systems with Energy Harvesting", IEEE International Green Computing

Conference (IGCC), pp. 1-6, 2011.

• Proposed solution: a semi-dynamic window-based scheduling

• Estimate/predict energy budget

• Preset execution strategy with uniform speed

• Divide execution process into schedule windows

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Motivation: Address Limitation of UTB

6 tasks finished

9 tasks finished

HOW? A spike/dip in harvesting

power can make the prediction

inaccurate.

Any mispredictions can only affect prediction accuracy of

one schedule window

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Proposed Semi-Dynamic Framework

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• During system execution for a long duration of time• Slice execution time into time windows of k minutes

• At reschedule point, predict/obtain energy budget for next time window

• Reject tasks based on energy budget. Then allocate the rest

• Execute accepted tasks with uniform optimal frequency

• Semi-Dynamic: reschedule→execute→reschedule→execute …

k minutes

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Experiment Setup

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• Design a simulation environment to capture workload executing on

multicore embedded system platform

• Historical weather data (solar radiation intensity, temperature)

provided by National Renewable Energy Laboratory (NREL)

• System only operates from 6:00 AM to 6:30 PM

• 50 periodic task sets are randomly generated for each comparison

• Implementation of our proposed Semi-Dynamic Algorithm (SDA),

together with Utilization-Based Algorithm (UTB)

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UTB: Utilization-Based Algorithm

J. Lu et al., “Scheduling and mapping of periodic tasks on multi-

core embedded systems with energy harvesting”, IGCC 2011.

• SDA outperforms UTB

• Advantage expands with increasing number of cores

• Up to 70% miss rate reduction compared to UTB

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100% utilization: very intensive workload

Simulation Results with Heavy Workload

Advantage of SDA: missUTB - missSDA

21• More miss rate reduction when power budget is low or fluctuating

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Related Topic I: Hybrid Energy Storage

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• Pros and cons of different storage medium types

battery-only Supercapacitor-only

high energy density low energy density

low power density high power density

less recharge cycles more recharge cycles

Solution:

Battery-supercapacitor hybrid storage system

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Simulation Results

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• CA-SDA outperforms BA-SDA

• Proposed HY-SDA outperforms all other techniques

UTB BA-SDA CA-SDA HY-SDA

best prior work battery-only supercap-only hybrid storage

Harvesting Power X 2

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• HY-SDA with penalty awareness outperforms MISS-SDA (HY-SDA

without priority support)

Related Topic II: Task Miss Penalty• Assume tasks have different miss penalties:

• Utilize flexibility of reschedule points provided by SDA

• During task rejection: give priority to tasks with higher miss penalty

density (miss penalty/required execution time)

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• Process variation:

• Variations in the attributes of transistors formed during fabrication

• Effect: performance metrics to deviate from their nominal values

• Variations in gate delays

• Solution: at reschedule point, distribute workload with awareness of

each core’s peak frequency

Related Topic III: Process Variation-Aware Workload Allocation

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1.0 1.0 1.0

0.8

0.80.8

1.00.6

0.6

→ different peak frequencies of cores

Simulation Results

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• Variation-Unaware: faulty schedule with high task miss rate

• Some assigned tasks consumed energy without finishing in time

• Variation-aware: significantly lower (up to 49%) task miss rate

• Peak frequencies of cores:

normal distribution with average of 1000MHz and variation of 33%

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Related Topic IV: Discrete Frequency Levels

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• In general, fexec selected by SDA is from a continuous range, which

may not correspond to an available, discrete frequency level

• Single: executing with discrete frequency level closest to and higher than fexec

Level 0 1 2 3 4 5

Frequency (MHz) idle 150 400 600 800 1000

Power (mW) 40 80 170 400 900 1600

• Proposed dual-speed method

• Intra: combine two neighboring

discrete frequencies to approximate

fexec

• Switch frequency multiple times

within each task instance

• Inter: avoid frequent speed switches

for less switching overhead

• Only switch frequency once between

every two task instances

Ends up with near-ideal result

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Related Topic V: Run-Time Thermal Issues

• Motivation

• Only passive cooling applicable

• Solar radiation → Tenviroment and fexecution

• High risk of core overheating during the middle of the day

• Solution:

• Passive: enforce thermal throttling for system stability

• Proactive: at reschedule point, allocate less workload to hotspot

• Advantages of proactive thermal management over passive:

• 22% less frequent thermal throttlings (94 → 74)

• Less throttlings→ more stable speed→ 2.7% miss rate reduction

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Summary of Contribution I:

Semi-Dynamic Scheduling for Independent Tasks

• Proposed a novel Semi-Dynamic Algorithm (SDA) that achieved

up to 70% miss rate reduction compared to best known prior work

• Unlike any other work, SDA provides flexibility to simultaneously

achieve multiple goals:

• Intelligent management of hybrid energy storage systems

• Priority scheduling for tasks with different miss penalties

• Process variation-aware workload allocation

• More energy efficient utilization of discrete frequency levels

• Proactive reaction to run-time thermal issues

• Publications Y. Xiang, S. Pasricha, "Run-Time Management for Multi-Core Embedded Systems with Energy

Harvesting", IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 2014.

Y. Xiang, S. Pasricha, "Harvesting-Aware Energy Management for Multicore Platforms with

Hybrid Energy Storage", ACM Great Lakes Symposium on VLSI (GLSVLSI), pp. 25-30, 2013.

Y. Xiang, S. Pasricha, "Thermal-Aware Semi-Dynamic Power Management for Multicore

Systems with Energy Harvesting", IEEE International Symposium on Quality Electronic Design

(ISQED), pp. 619-626, 2013.

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OUTLINE

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Conclusion Ou

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Contribution II:

Template-Based Scheduling for Task Graphs

• Objective

• To reduce total task graph miss rate/penalty under variant and

stringent energy harvesting condition at run-time

• To offload scheduling complexity of task graphs to design time

• Contribution

• A hybrid workload management framework (HyWM) using schedule

templates to integrate design-time and run-time scheduling efforts

• Two approaches for offline schedule template generation

• Novel run-time slack reclamation and soft error handling heuristics

• Aging-aware workload allocation on multicore processors

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• Multiple applications modeled as periodic task graphs

• Each periodic task graph has instances that arrive recursively

• To avoid a miss: finish all task nodes before deadline

Workload: Periodic Task Graphs

1st period 2nd period 3rd period

arrival times deadlines

new TG instance ready finished or missed ?32

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• Task scheduling on systems powered by energy harvesting

• IGCC 2011’ J. Lu and Q. Qiu

Scheduling and Mapping of Periodic Tasks on Multi-Core Embedded

Systems with Energy Harvesting

No awareness of task dependency

• Energy-aware scheduling for task graphs

• DATE 2007’ R. Wtanabe et al.

Task scheduling under performance constraints for reducing the energy

consumption of the multi-processor SoC

Does not target systems with energy harvesting

The first work to consider scheduling of multiple task graphs

for systems powered by energy harvesting

Related Works

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Hybrid Workload Management: Motivation and Overview

Template-based:

• Design-time (offline) schedule template generation to offload complexity

• Run-time (online) schedule template selection based on energy budget

With variations in energy harvesting, how to provide fixed energy

budget so that we know which template to select?

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Motivation for hybrid framework:

• Task graph scheduling problem is complex

• Limited energy and computing resource prohibit dynamic scheduling

at run-time

Semi-Dynamic Framework

• Semi-dynamic: divide time of execution into schedule windows

• Each window has an energy budget that is independent from

other windows

• Shifting: use energy harvested during the previous window

• Each window has an energy budget that is decoupled from

run-time variations in energy harvesting 35

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Design Time Template Generation:

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• Optimized schedule template

• Time consuming

• Design-time

MILP-Based Template Generation

Mixed Integer Linear Programming

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MILP for Design-Time Template Generation

• Represent scheduling problem as mixed-integer linear programming

(MILP) formulation

• Objective:

• Formulate MILP constraints based on workload, platform, and energy

budget

• Represent scheduling decisions as integer variables in MILP

• Generated templates saved in system for run-time selection

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MILP

Optimization

Optimized

Schedule

Templates

Energy budget levels

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Quality of Schedule Templates Generated by MILP

• With low energy budget, schedule less workload for higher efficiency

• With sufficient energy budget, schedule full workload even with

stringent timing requirement (95% per core utilization)

• 4-core embedded system

• 9 task graph instances per schedule window (E3S benchmark)

• With per-core average utilization of 95%

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Frequency Selection in Schedule Templates

Dealing with stringent energy constraint when energy budget is low

• Lower energy budget → lower average execution frequency

• Slow and inefficient frequency level (150MHz) is ignored

automatically

• Breakdown of frequencies selected for all task nodes

• For schedule templates with energy beget levels from low to high

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Frequency Selection in Schedule Templates

Dealing with stringent timing constraint when energy budget is high

• Higher workload intensity → higher average execution frequency

• Breakdown of frequencies selected for all task nodes

• For schedule templates with energy beget levels from low to high

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• Task scheduling on systems powered by energy harvesting

• IGCC 2011’ J. Lu and Q. Qiu

Scheduling and Mapping of Periodic Tasks on Multi-Core Embedded

Systems with Energy Harvesting

• GLSVLSI 2013’ Y. Xiang and S. Pasricha

Harvesting-Aware Energy Management for Multicore Platforms with

Hybrid Energy Storage

• Energy-aware scheduling for task graphs

• DATE 2007’ R. Wtanabe et al.

Task scheduling under performance constraints for reducing the energy

consumption of the GALS multi-processor SoC

• Embedded System Synthesis Benchmarks Suite (E3S)

Comparison with Prior Work

UTA

SDA

LP+SA

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Linear programming + heuristics

Assume that scheduler can identify ready task nodes for them

Comparison over Miss Rate

• Proposed HyWM-LP: miss rate reduction of 9.7% compared to

LP+SA, 15.2 % compared to SDA, and 29.5% compared to UTA

• Because of semi-dynamic framework: VS

• Because of dependency awareness: VS

• Because of schedule template optimality : VS42

dependency Window-

Shifting

UTA

SDA

LP+SA

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Alternative Approach

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• Time consuming

• Not Scalable


• Near-optimal schedule template

• Much less time consuming

• Better scalability

Analysis-Based Template Generation

ATGMILP

For problem size of 10 tasks, 100 nodes:

about 100 hours and 5 GB memory

Fast schedule template generation

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Analysis-Based Template Generation (ATG)

Basic ideas:

• With known workload and platform, we can simulate system execution

• During simulation, we can monitor execution process to find energy inefficient events

• We can get hindsight on how to avoid these events

• Make informed update on execution schedule and rewind simulation

• Save the best schedule we have

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Dynamic Critical Path Identification in ATG

• Start from the ending task node with deadline equal to deadline of task graph

• Recursively calculate implicit deadlines of precedent task nodes:

To compare priorities of task nodes

To identify critical paths of task graphs

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CO

MM

= 5

0

WCET = 1100

Implicit Deadline = 2000 – 100 – 150

Implicit Deadline = 1750

τ1

τ3τ2

τ4

Task Graph Deadline = 2000

WCET = 800



WCET = 300

1850 – 800 – 50 > 1750 – 1100 – 200



WCET = 100

Implicit Deadline =

2000

Direction of

Calculation

Other Predecessor Nodes in Task

Graph

(Omitted in this figure)

Initially only one deadline

for entire task graph

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Motivation for Alternative Approach

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• Time consuming

• Not Scalable


• Near-optimal schedule template

• Much less time consuming

• Better scalability

Analysis-Based Template Generation

ATGMILP

For problem size of 10 tasks, 100 nodes:

about 100 hours and 5 GB memory Near-optimal schedule templates (up to 1.3%

higher miss rate)

Only uses about 1 hour and 50 MB memory

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Fast schedule template generation

• Random transient errors (bits flipping) in circuits, caused by

• Alpha particles from package decay

• Neutron strikes from cosmic rays

• Not a permanent failure in circuitry

• May lead to faulty/invalid execution result on a task node

(also considered as task graph miss → waste of energy)

Related Topic I: Soft Errors

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Earth’s Surface

pn p

p

n

n

p

pn

nn+

- ++ +-- -

Transistor Device

source drain

Particle Strike!

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Run-Time Soft Error Handling and Slack Reclamation

• Goal: avoid invalidation of schedule templates

• Never change allocation and execution order decisions

• Never delay start times of task graph instances

• Can drop task instances, adjust execution frequencies when necessary

• Soft error handling heuristic

• Triggered when a soft error is detected on a task node

• Attempts to re-execute node with error to save previous execution effort

• By boosting up frequency using surplus energy

• By dropping of upcoming and not started yet task graphs

• By reclaiming slack time

• Slack reclamation heuristic

• To utilize slack time due to task dropping and earlier than worst-case execution time

• Triggered when a task node can start earlier than scheduled

• Reclaim: slow down execution frequency without delaying scheduled finish time

• Pass-on: If can’t reclaim instantly, start and finish task earlier

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Benefit of Slack Reclamation and Error Handling

Setup:

• Based on 8-core system

• Execution time variation: uniform distribution from 50% to 100% of worst case

• Error injection rate: 10-5/s per core at maximum frequency

Experiment Results:

1. Base case: no slack time awareness + no soft error awareness:

High miss rate of 40.11%

2. Slack reclamation + no soft error awareness:

Significant lower miss rate of 29.19% (27.2% reduction)

3. Slack reclamation + soft error handling:

miss rate of 22.01% (45.2 % reduction)

In all, 45.2% miss rate reduction achieved compared to the base case

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• A major concern for electronic devices with technology scaling

Related Topic II: Aging Effect

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Useful lifeInfant

mortality

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Per-Core Aging Effect Modeling

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Infant

mortality

~ 10 year

Reliability model based on Weibull distribution

• Core survival rate at time t:

• β, constant parameter related

to core architecture

• Scale parameter, α

• Electromigration model:

• Workload-related factors: supply voltage (vdd), execution frequency (f),

core temperature (T)

• Can be controlled

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• Assumes 8-core system with per-core aging progress detection circuitry

• Aging-Aware: to balance workload/aging effect among cores

• Compare against:• Biased: always assign heavier workload to certain cores

• Random: randomize workload allocation

Aging-Aware Allocation Scheme

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~ 10 year

• Aging-Aware: slower reliability drop and improved MTTF (+19.7%)

MTTF: mean-time-to-failure

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• Experiment:• 8-core homogenous processor• Processing capability : achievable task graph finish rate with remaining cores

System Aging Model with Core Failure Tolerance

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Failure Threshold* 0 1 2 3 4 5 6 7

MTTF (years) 10.06 15.58 20.22 24.66 29.27 34.44 40.94 51.35

Processing Capability

Before System Failure (%)100 92.3 80.1 68.8 52.4 34.9 18.4 7.0

Average Processing

Capability during System

Lifetime (%)

100 96.5 92.5 87.7 81.7 75.0 67.3 60.3

• Higher tolerance → longer MTTF → lower performance guarantee

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• Multicore system reliability model:

With different levels of tolerance on number of failed cores (h)

Probability of exactly h core survived after tw schedule windows

Summary of Contribution II:

Template-Based Scheduling for Task Graphs

• The first work to consider task graph set scheduling for multiprocessors powered by energy harvesting

• Hybrid Workload Management framework (HyWM) achieved up to 29.5% lower miss rate compared to prior works

• Two offline schedule template generation approaches to offloadscheduling complexity of task graphs to design-time

• Comprehensive heuristic for slack reclamation and soft error handling with up to 45.2% miss rate reduction

• Aging-aware workload allocation with 19.7% longer MTTF

• Publication Y. Xiang, S. Pasricha, "Fault-Aware Application Scheduling in Low Power Embedded

Systems with Energy Harvesting", ACM/IEEE International Conference onHardware/Software Codesign and System Synthesis (CODES+ISSS), article 32, 2014.

Y. Xiang, S. Pasricha, "A Hybrid Framework for Application Allocation and Schedulingin Multicore Systems with Energy Harvesting", ACM Great Lakes Symposium on VLSI(GLSVLSI), pp. 163-168, 2014.

Y. Xiang, S. Pasricha, "Soft and Hard Reliability-Aware Scheduling for MulticoreEmbedded Systems with Energy Harvesting", IEEE Transactions on Multi-ScaleComputing Systems (TMSCS), under review.

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OUTLINE

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Conclusion Ou

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Contribution III: Mixed-Criticality Scheduling on Heterogeneous Systems

• Main Objective

• To reduce miss penalty of mixed-criticality workload under varying

and stringent energy harvesting condition

• Contributions

• Modeled mixed-criticality workload with soft/firm deadline constraints

• A single-ISA heterogeneous multicore platform for mixed-criticality

scheduling

• A novel timing intensity metric to estimate task instance importance

in a mixed-criticality workload

• Considered near-threshold computing for high energy efficiency

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Mixed-Criticality Workload

(m, k)-soft deadline: only need to finish m instances out of every k instances

• Compare throughput-centric workload to timing-centric workload:

• Has less critical timing requirements

• Emphasizes even more on energy efficiency

• Our mixed-criticality scheduling problem:

Simultaneous scheduling of timing- and throughput-centric tasks

with energy harvesting

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Criticality Type Timing-Centric Throughput-Centric

Structure Model task graphs multithreaded applications

Parallelism highly customized barrier-synchronized

Execution Time few seconds few minutes

Period tens of seconds tens of minutes

Deadline Model firm (m, k)-soft

Schedule Method template-based dynamic scheduling

Benchmark Suit E3S PARSEC

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special case of task graph

simpler parallelism

longer execution time

period > schedule window

flexible timing

possible and necessary

same as task graph model

used in the previous section

Mixed-Criticality Scheduling on Heterogeneous Platform

• Big cores (high performance) for timing-centric task graphs

• Small cores (high efficiency) for throughput-centric multithreaded

applications: near-threshold computing

• Exception: sequential phases of multithreaded applications executed on big cores

• Interaction: split energy budget by comparing miss penalty density

• Lower weight factor (timing intensity) for tasks with soft deadlines

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Near-Threshold Computing

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Vth: CMOS gate-to-source

threshold voltage

Vdd: supply voltage:

• Super-threshold region

• High performance

• Low efficiency

• Near-threshold region

• Lower performance

• Highest efficiency

Heterogeneous Platform: Big Core and Small Core

• Simulated using Sniper (performance) and McPAT (power)

• Small core (near-threshold supply voltage) to focus on energy efficiency

• Big core to focus on timing performance

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Architectural Parameters

Core Types Big Cores Small Cores

Execution Out-of-Order In-Order

Issue Width 4 2

Reorder Buffer Size 128 N/A

Cache 64KB, 4-way 16KB, direct

Core Area 15.7 mm2 4 mm2

Cluster Parameters

Cluster Type Big-Core-Cluster Small-Core-Cluster

Core Count 8 32

Frequency Control Per-Core DVFS Uniform Frequency

f , Vdd Range 0.5~1.2GHz, 0.4~1 V f nth, Vddnth

Technology Parameters

Technology Node 22 nm

Vth 0.289 V

Vddnth, f nth 0.4 V, 500 MHz

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Experiment Results

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• Compared to (m, k)-unaware,

9.5% performance benefit from soft deadline-awareness

• Compared to PIE (Performance impact estimation, ISCA 2012),

13.6% performance improvement from emphasis on energy efficiency

• Compared to B8-B8,

23.2% performance benefit from heterogeneous computing

B8-S32: proposed mixed-critical scheduling on heterogeneous platform with 8 big

cores and 32 small cores

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Craeynestet al., “Scheduling heterogeneous multi-cores

through Performance Impact Estimation”, ISCA 2012.

• First work to consider mixed-criticality scheduling and near-threshold

computing for energy harvesting embedded systems

• A single-ISA heterogeneous multicore platform for mixed-criticality scheduling

• Estimate importance of tasks based on soft deadline constraints

• Up to 23.2% performance benefit

• Publication• Y. Xiang, S. Pasricha, "Mixed-Criticality Scheduling on Heterogeneous Multicore Systems

Powered by Energy Harvesting", ACM Transaction on Embedded Computing (TECS), under

review.

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Summary of Contribution III: Mixed-Criticality Scheduling on Heterogeneous Systems

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OUTLINE

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Conclusion Ou

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• The proposed Semi-Dynamic Framework is a unified solution for

energy harvesting-aware resource management with significant

advantages on energy efficiency and flexibility over prior work

• Issues addressed by our Semi-Dynamic Framework:

1. Minimizing miss rate/miss penalty

2. Run-time thermal control

3. Mitigating impact of process variation

4. Management of hybrid energy storage

5. Scheduling of task graphs with inter-node dependencies

6. Soft error handling and slack reclamation during execution

7. Mitigating aging effects across the chip over time

8. Mixed-criticality scheduling on heterogeneous processors

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My Dissertation Summary

List of PublicationsJournal papers:

• Y. Xiang, S. Pasricha, "Run-Time Management for Multi-Core Embedded Systems with Energy

Harvesting", IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 2014.

• B. Donohoo, C. Ohlsen, S. Pasricha, C. Anderson, Y. Xiang, "Context-Aware Energy

Enhancements for Smart Mobile Devices", IEEE Transactions on Mobile Computing (TMC), vol.

13, no. 8, 2013.

• Y. Zou, Y. Xiang, S. Pasricha, "Characterizing Vulnerability of Network Interfaces in Embedded

Chip Multiprocessors", IEEE Embedded System Letters (ESL), vol. 4, no. 2, 2012.

• Y. Xiang, S. Pasricha, "Mixed-Criticality Scheduling on Heterogeneous Multicore Systems

Powered by Energy Harvesting", ACM Transaction on Embedded Computing (TECS), under

review.

• Y. Xiang, S. Pasricha, "Soft and Hard Reliability-Aware Scheduling for Multicore Embedded

Systems with Energy Harvesting", IEEE Transactions on Multi-Scale Computing Systems

(TMSCS), under review.

Conference papers:

• Y. Xiang, S. Pasricha, "Fault-Aware Application Scheduling in Low Power Embedded Systems

with Energy Harvesting", ACM/IEEE International Conference on Hardware/Software Codesign

and System Synthesis (CODES+ISSS), article 32, 2014.

• Y. Xiang, S. Pasricha, "A Hybrid Framework for Application Allocation and Scheduling in Multicore

Systems with Energy Harvesting", ACM Great Lakes Symposium on VLSI (GLSVLSI), pp.163-

168, 2014.

• Y. Xiang, S. Pasricha, "Harvesting-Aware Energy Management for Multicore Platforms with

Hybrid Energy Storage", ACM Great Lakes Symposium on VLSI (GLSVLSI), pp. 25-30, 2013.

• Y. Xiang, S. Pasricha, "Thermal-Aware Semi-Dynamic Power Management for Multicore Systems

with Energy Harvesting", IEEE International Symposium on Quality Electronic Design (ISQED),

pp. 619-626, 2013.

• Y. Zou, Y. Xiang, S. Pasricha, “Analysis of On-chip Interconnection Network Interface Reliability in

Multicore Systems”, IEEE International Conference on Computer Design (ICCD), pp.427-428,

2011.

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Thank You!

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Documents

Yi Xiang · 2015. 9. 9. · energy harvesting-aware multicore embedded system platforms • Solar energy harvesting as the only power source • Batteries/supercapacitors used for