SACR: Scheduling-Aware Cache Reconfiguration for Real-Time Embedded Systems

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SACR Scheduling-Aware Cache Reconfiguration for
Real-Time Embedded Systems

• Weixun Wang and Prabhat Mishra
• Embedded Systems Lab
• Computer and Information Science and Engineering
• University of Florida
• Ann Gordon-Ross
• Electrical and Computer Engineering
• University of Florida

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Outline

• Introduction
• Related Work
• Scheduling-Aware Cache Reconfigurations
• Phase-based Optimal Cache Selection
• Scheduling-Aware Dynamic Reconfigurations
• Experiments
• Conclusion

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Introduction

• Real-time embedded systems
• Energy constraints (battery operated)
• Time constrained
• Hard task deadlines
• Safety-critical systems
• Soft task deadlines
• Gaming, multimedia, housekeeping devices have
  soft deadlines
• Deadline miss results in temporary
  service/quality degradation
• Dynamic cache reconfigurations
• Promising for improving energy and performance
• Not applicable in real-time systems
• Dynamic computation is expensive
• Dynamic reconfiguration leads to timing
  uncertainty

Propose a scheduling-aware dynamic cache
reconfiguration technique to generate
significant energy savings in soft real-time
systems by exploiting static analysis during
runtime.
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Outline

• Introduction
• Related Work
• Scheduling-Aware Cache Reconfigurations
• Phase-based Optimal Cache Selection
• Scheduling-Aware Dynamic Reconfigurations
• Experiments
• Conclusion

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Related Work

• Energy-aware task scheduling techniques
• Early Deadline First (EDF) and Rate Monotonic
  (RM)
• Dynamic Voltage Scaling (DVS)
• Jejurikar et al. IEEE TCAD 06, Quan et al.
  ACM TECS 07
• Caches in Real-Time Systems
• Cache locking and Cache Partitioning
• Puant RTSS 02 and Wolfe IWRCS 1993
• Cache-related preemption delay analysis
• Tan et al. ACM TECS 2007
• Reconfigurable Cache Architectures
• Reconfigurable cache architecture
• Zhang et al. ACM TECS 05
• Application-based vs. Phase-based tuning
• Gordon-Ross et al. ISLPED 05 vs. Sherwood et
  al. Micro 03

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Outline

• Introduction
• Related Work
• Scheduling-Aware Cache Reconfigurations
• Phase-based Optimal Cache Selection
• Scheduling-Aware Dynamic Reconfigurations
• Experiments
• Conclusion

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Overview
Task 1
Task 2
In traditional real-time systems
In our approach
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Phase-based Optimal Cache Selection

• A task is divided by n potential preemption
  points
• A phase is the period of time between a
  predefined potential preemption point and task
  completion
• Each phase has its optimal cache configuration
• Performance-optimal and energy-optimal
• A static profile table is generated for each task

Pn-1
P1
P2
0
Task Execution Time
phase n (n-1/n)
Cn
phase 3 (2/n)
C3
phase 2 (1/n)
C2
phase 1 (0/n)
C1
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Phase-based Optimal Cache Selection

• Potential preemption points may not be the same
  as actual preemption points.
• They are used for cache configuration selection.
• Partition factor determines the potential
  preemption points and resulting phases
• Large partition factor leads to large look-up
  table
• Not feasible due to area constraints
• Large partition factor may not save more energy
• Partition factor around 4 to 7 is profitable

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Outline

• Introduction
• Related Work
• Scheduling-Aware Cache Reconfigurations
• Phase-based Optimal Cache Selection
• Scheduling-Aware Dynamic Reconfigurations
• Statically Scheduled Systems
• Dynamically Scheduled Systems
• Experiments
• Conclusion

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Scheduling-Aware Cache Reconfiguration

• Statically scheduled systems
• Arrival times, execution times, and deadlines are
  known a priori for each task
• Statically profile energy-optimal configurations
  for every execution period of each task without
  violating any task deadlines
• Dynamically scheduled systems
• Task preemption points are unknown
• New tasks can enter the system at any time
• Conservative approach
• Aggressive approach

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Conservative Approach

• Energy-optimal cache configuration with equal or
  higher performance than base cache
• Nearest-neighbor
• Use the nearest partition point to decide which
  cache configuration to tune to

• Static Profile table
• Deadline-aware energy-optimal configurations
• Task list entry
• Runtime information

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Aggressive Approach

• Energy-optimal cache configuration
  Performance-optimal cache configuration
• Includes their execution time as well.
• Ready task list (RTL)
• Contains all the tasks currently in the system

• Static Profile table
• Energy-opt configuration
• Perf.-optimal configuration
• Task list entry
• Runtime information

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Outline

• Introduction
• Related Work
• Scheduling-Aware Cache Reconfigurations
• Phase-based Optimal Cache Selection
• Scheduling-Aware Dynamic Reconfigurations
• Experiments
• Conclusion

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Experimental Setup

• SimpleScalar to obtain simulation statistics
• Used external I/O (eio) trace file, checkpointing
  and fastforwarding to generate static profile
  table
• Energy model
• Zhang et al. and CACTI 4.2
• Benchmarks
• EEMBC
• MediaBench

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Energy Savings (Instruction Cache)
28 average energy savings using conservative
approach
51 average energy saving using aggressive
approach
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Energy Savings (Data Cache)
17 average energy savings using conservative
approach
22 average energy saving using aggressive
approach
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Hardware Overhead

• Profile Table stores 18 cache configurations
• Synthesized using Synopsys Design Compiler
• Assumed lookup frequency of one million
  nanoseconds
• Table lookup every 500K cycles using 500 MHz CPU
• Average energy penalty is 450 nJ
• Less than 0.02 of overall savings (2825563 nJ)

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Conclusion

• Dynamic cache reconfiguration is a promising
  approach to improve both energy consumption and
  overall performance.
• Developed a scheduling aware dynamic cache
  reconfiguration technique
• On average 50 reduction in overall cache energy
  consumption in soft real-time systems
• Future work
• Hard real-time systems
• Multi-core and multi-processor systems

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• Thank you !

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Aggressive Approach

• When task T is the only task in the system
• Always tune to energy-optimal cache if possible
• When task T preempts another task
• Run schedulability check
• Discard the lowest priority task if absolutely
  necessary
• Tune to energy-optimal cache
• if all other tasks in RTL can meet their
  deadlines using their performance-optimal caches
• When task T is preempted by another task
• Calculate and store runtime information (RIN, CP)