Embedded System Design Framework for Minimizing Code Size and Guaranteeing RealTime Requirements

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Embedded System Design Framework for Minimizing
Code Size and Guaranteeing Real-Time Requirements

• Insik Shin, Insup Lee, Sang Lyul Min

CIS, Penn, USA
CSE, SNU, KOREA
The 23rd IEEE International Real-Time Systems
SymposiumDecember 3-5Austin, TX. (USA)
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Outline

• Design problem in real-time embedded systems
• motivation
• problem statement
• Solution design framework
• overview
• problem formulation
• heuristic solutions and evaluations
• Conclusion

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Code Size Reduction in Embedded Systems

• Code size is a critical design factor
• For many embedded systems, code size reduction
  can affect their design and manufacturing cost.
• Code size reduction technique at ISA level
• a subset of normal 32-bit instructions can be
  compressed into a 16-bit format as in ARM Thumb
  and MIPS 16.
• code size can be reduced by 30, while the number
  of instructions can increase by 40.

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Code Size vs Execution Time Tradeoff

• Code size (s) / execution time (e) tradeoff

• a program unit (function or basic block) can be
  compiled into 16 or 32 bit instructions.

• then, we can obtain a list of possible (s, e)
  pairs for each program

Program
32 bit
16 bit
32 bit
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Tradeoff Function

• Discrete tradeoff function for each task ?i
• with the list of possible (s, e) pairs for each
  program (task), we can define a discrete tradeoff
  function s fi(e) for each task.

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Tradeoff Function

• Linear approximated tradeoff function
• we can safely approximate the discrete tradeoff
  function with a linear tradeoff function.

s
e
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Challenging Problem

• Given the code size vs. execution time tradeoff
  of each task in a real-time embedded system,

• a natural problem is
• minimizing the total code size of the system
• while guaranteeing all the temporal
• requirements imposed on the system.

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Our Approach

• Much work on the real-time system design
  framework guaranteeing the system temporal
  requirements.
• Traditional design frameworks are for minimizing
  the system utilization, while our problem aims at
  minimizing the system code size.
• Instead of solving the problem from the scratch,
  we chose to extend a traditional real-time design
  framework considering code size minimization.

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Period Calibration Method (PCM)

• A popular design framework that transforms
  real-time system requirements into real-time task
  scheduling parameters while
• guaranteeing the system timing requirements
• minimizing the system utilization
• R. Gerber et al. Guaranteeing End-to-End Timing
  Constraints by Calibrating Intermediate
  Processes, RTSS 94.

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Period Calibration Method (PCM)

• System Requirements Task Parameters
• Guaranteeing the system end-to-end timing
  requirements
• Minimizing the utilization

System Requirements
Task Precedence
PCM
Task Parameters
End-to-End Timing Requirements
Period, Offset, Deadline, Fixed Priority
Task Execution Time
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Overview of Our Approach

• System Requirements Task Tradeoff Task
  Parameters
• Guaranteeing the system end-to-end timing
  requirements
• Minimizing the total code size

System Requirements
Design Framework
Task Precedence
End-to-End Timing Requirements
Task Parameters
Task Tradeoff
Period, Offset, Deadline, Execution Time, Code
Size

Task 1
Task 2
Task n
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Design Framework Overview
Design Framework
System Requirements
Task Execution Time
Task Tradeoff
Task Parameters
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Design Framework Feasibility Analysis

• Feasibility Analysis
• for all time intervals t1,t2, the amount of
  execution to be done within the interval is no
  greater than the interval length,

• Task model
• asynchronous periodic tasks with pre-period
  deadlines under EDF scheduling

• this feasibility analysis is NP-hard Baruah
  90.

?1
?2
?3
0
5
10
15
20
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Design Framework Feasibility Analysis

• Synchronous time interval
• starts at the release time of a job and ends at
  the deadline of a job.

• Feasibility Analysis

• for all possible time intervals

• for all synchronous time intervals

?1
?2
?3
0
5
10
15
20
t2
t1
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Design Framework Optimization Framework

• Optimization problem
• objective minimizing
• constraint feasibility
• for all synchronous time intervals t1, t2

We want to determine task execution time to
minimize the total code size while guaranteeing
feasibility.

• it is a form of a LP problem with linear tradeoff

• regardless of feasibility analysis complexity,
  it is NP-hard with discrete tradeoff

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Design Framework Optimization Framework

• Heuristics for solving the optimization problem
• Highest Best Reduction-Ratio First (HBRF)
• favors a task that reduces its code size the most
  with the same amount of execution time increase
• Longest Period First (LPF)
• favors a task with the longest period
• Highest Best Weighted-Reduction-Ratio First
  (HBWF)
• combines HBRF and LPF
• Complexity
• HBRF HBWF O(nh), LPF O(n)
• n of tasks, h of tradeoff values
• Performance evaluation through simulation

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Simulation

• Simulation parameters
• period 10, 20, 25, 50, or 100 ms
• offset deadline randomly chosen according to
  period
• 5 pairs of code size/execution time tradeoff are
  randomly chosen according to offset, deadline,
  and period
• 4, 6, 8, 10, and 12 tasks (more than 100 times
  each)
• Simulation measure - closeness to OPT
• RA the reduced amount of total code size
• closeness to OPT

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Performance of algorithms with 8 tasks
Closeness to OPT ()
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Performance of BEST with various task numbers
Closeness to OPT ()
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Conclusion

• Design framework taking advantage of the code
  size vs. execution time tradeoff
• Future work
• To develop an integrated approach and to evaluate
  the complexity and effectiveness.
• To extend this framework so as to utilize
  tradeoffs among code size, execution time, and
  energy consumption.