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Final Presentation
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FINAL PROJECT PRESENTATIONSubmitted by
Pranav S Devalla829-844-527
Literary Review
on Energy- and
Thermal- aware
task allocation
on SoCs
Abstract
0 The aggressive semiconductor technology scaling has been pushing the device feature size into the deep sub-micron region.
0 As a result, the chip power density has been doubled every two to three years.
0 This increased power has directly translated into high temperature, which negatively affects a system's cost, performance and reliability.
0 In this review, various methodologies for thermal and energy problem mitigation are presented and compared
Power Consumption Issues0 Stress on batteries in portable devices such as laptops and phones
0 Can be minimized through voltage and frequency scaling
0 High temperature greatly shortens the lifespan of a processor 0 100C increase in temperature reduces component life by 50% [1]
0 Obvious approach is to use bigger heat sinks and air- cooling techniques (for desktop and laptop computers)0 Expensive and inefficient
0 Power- aware techniques are not efficient in handling these issues0 Logic blocks within the chip have different power densities (e.g. due to
different levels of switching activity) 0 The thermal map of a chip often shows wide variations in temperature0 Many low-power techniques have insufficient impact because they do not
directly target the spatial and temporal behavior of the operating temperature.
Thermal- aware Computing [2]
0 Components of power consumption0 Dynamic
0 consumed when devices switch from one logic level to another.0 related to the level of computational (switching) activity
0 Leakage0 power that flows from source to ground whenever a device is powered up0 grows exponentially with temperature
0 Thermal modeling0 Hotspot Heatflow model [3]
Thermal- aware Computing
0 Thermal- aware chip design (Static)0 focus on the floorplanning phase of the physical design process [4,5,6,7]
0 Floorplanning algorithms can be modified to also include reducing the maximum temperature of a block in the chip.
0 Migration Computing [8]0 Increasing silicon area allocated to hotblocks [9]
0 Runtime Thermal Management (Dynamic)0 The operating system controls the scheduling of tasks and also assign tasks to
individual cores0 Heat Balancing0 Heat Unbalancing0 Reducing Execution Rate of Hot Tasks0 Adding a Predictive Component
Thermal- aware Computing
Runtime Techniques Methodology
Voltage Scaling Change voltage levels to adjust power and energy
consumption. Clock rates are reduced to match the
increased circuit delay that results
Heat Balancing Spreads the thermal load among multiple cores to
approximately even out their temperatures.
Heat Unbalancing Reduce thermal cycling effects: accept significant
temperature differentials between the cores as long as
specified temperature levels are not breached.
Throttling Reduce the rate at which heat is generated byreducing instruction fetch rate and similar
parameters.
Thermal- aware Scheduling
0 Thermal aware task allocation in SoCs
0 Dynamic Thermal Management through Task-Scheduling [18]0 Thermal-Aware Task Allocation and Scheduling for Embedded Systems [19]0 Static and Dynamic Temperature-Aware Scheduling for Multiprocessor SoCs
[20]
Thermal-Aware Task Allocation and Scheduling for Embedded Systems (Hung
et. al)
0 Proposed an algorithm that is used as a subroutine for hardware/software co-synthesis0 To exploit resource sharing
0 Traditional algorithms do not take the temperature and power variables into consideration
0 Power awareness0 Dynamic Criticality (DC)
0 Analogous to priority
0
Thermal-Aware Task Allocation and Scheduling for Embedded Systems (Hung
et. al)0 The flows of the thermal-aware co-synthesis framework and thermal-aware platform-based system design
0 The temperature comparisons of the power-aware and the thermal-aware approaches on co-synthesis architecture.
Static and Dynamic Temperature-Aware Scheduling for Multiprocessor SoCs (Coskun et. Al)
0 This looks at Multiprocessor SoCs
0 ILPs to generate static solutions 0 target thermal hotspots and gradients 0 better thermal profile than other static methods
0 Dynamic Scheduling (OS- level scheduling)0 Adaptive –random technique
Static and Dynamic Temperature-Aware Scheduling for Multiprocessor SoCs (Coskun et. Al)
Dynamic Thermal Management through Task-Scheduling (Yang et.
al)0 ThreshHot Algorithm
0 reduces the number of hardware DTMs (Dynamic thermal management) required.
0 Increase in CPU throughput
Dynamic Thermal Management through Task-Scheduling (Yang et.
al)
Comparative TableAuthors Methodology Static Thermal
ManagementDynamic thermal
Management
Static Energy Management
Dynamic Energy
Management
Issues
Hung et. al Implemented algorithm with
temperature and power
vaiables
No Yes No Yes Floorplanning is not effective to
control the lateral heat
transfer. Overhead due to dynamic nature
Coskun et. al Implemented adaptive –
random scheduling algorithm
Yes Yes No Yes Overhead associated with
dynamic awareness is
high
Yang et. al Implemented ThresHot
scheduling algorithm
No Yes No Yes Overhead associated with
dynamic awareness is
high
Energy- aware Computing
0 Energy consumption is a critical measure for battery powered and tethered devices.
0 Energy can be reduced by0 Static 0 Dynamic
0 DVFS
0 Examples0 idle functional units can be powered down [10]0 clock gating [11]0 low-power design [12]0 low-power synthesis [13]0 lower the operating voltage level during the design/synthesis phase [14]
Energy- aware Computing
0 Energy- aware task scheduling 0 EDF [16]0 RM [17]0 LEDF
0 Energy- aware task scheduling in SoCs
0 Energy-Aware Task Allocation for Rate Monotonic Scheduling [21]0 Real-time task scheduling for energy-aware embedded systems [22]0 Energy-Aware Runtime Scheduling for Embedded Multiprocessor SOCs [23]
Energy-Aware Task Allocation for Rate Monotonic Scheduling
(AlEnawy et. al)0 adopt partitioned scheduling and assume that tasks are assigned static
(rate-monotonic) priorities.
0 study and evaluate a number of well-known partitioning heuristics, RMS admission control algorithms, and speed assignment schemes in terms of the feasibility performance and overall energy consumption.
0 Off-line and on-line partitioning
Energy-Aware Task Allocation for Rate Monotonic Scheduling
(AlEnawy et. al)
Real-time task scheduling for energy-aware embedded systems
(Swaminathan et. al)0 Two on-line scheduling algorithms that attempt to
minimize the energy consumed by a periodic task set
0 Both using EDF
0 LEDF
0 E- LEDF
Real-time task scheduling for energy-aware embedded systems
(Swaminathan et. al)
Energy-Aware Runtime Scheduling for Embedded Multiprocessor SOCs (Yang et.
al)
0 Preorder the concurrent behavior as much as possible
0 This task-scheduling method for embedded systems combines the low runtime complexity of a design-time scheduling phase with the flexibility of a runtime scheduling phase.
0 increases design flexibility and reduces design time for multiprocessor SOCs
Energy-Aware Runtime Scheduling for Embedded Multiprocessor SOCs (Yang et.
al)
Comparative Table
Authors Methodology Static Energy Management
Dynamic Energy Management
Issues
AlEnawy et. al Partitioned task scheduling with static priorities
Yes Yes Does not have good performance for on-line
partitioning and overhead due to
dynamic computations
Swaminathan et. al Implemented on-line scheduling
algorithms based on EDF
No Yes Difficulty with Aperiodic and sporadic tasks
and overhead due to dynamic
computations
Yang et. al Algorithm combines the low runtime complexity of a
design-time scheduling phase
with the flexibility of a runtime scheduling
phase.
No Yes Ineffective for very heavy loads and
difficult to implement for
practical applications