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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 Symposium December 3-5 Austin, TX. (USA). Outline.

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Embedded system design framework for minimizing code size and guaranteeing real time requirements

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)


Outline
Outline and

  • Design problem in real-time embedded systems

    • motivation

    • problem statement

  • Solution – design framework

    • overview

    • problem formulation

    • heuristic solutions and evaluations

  • Conclusion


Code size reduction in embedded systems
Code Size Reduction in Embedded Systems and

  • 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%.


Code size vs execution time tradeoff
Code Size vs Execution Time Tradeoff and

program unit

s

e

  • 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

32 bit

16 bit


Tradeoff function
Tradeoff Function and

s

e

  • 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.


Tradeoff function1
Tradeoff Function and

  • Linear approximated tradeoff function

    • we can safely approximate the discrete tradeoff function with a lineartradeoff function.

s

e


Challenging problem
Challenging Problem and

Real-time embedded system

Task Tradeoff

Task 1

Task 2

Task n

  • 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.


Our approach
Our Approach and

  • 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.


Period calibration method pcm
Period Calibration Method (PCM) and

  • 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.


Period calibration method pcm1
Period Calibration Method (PCM) and

1

3

X1

d1

Y1

2

4

X2

d2

  • 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


Overview of our approach
Overview of Our Approach and

  • 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


Design framework overview
Design Framework Overview and

PCM

Period, Offset, Deadline

Feasibility

Analysis

Feasibility

Constraint

Optimization Framework

Execution Time, Code Size

Design Framework

System Requirements

Task Execution Time

Task Tradeoff

Task Parameters


Design Framework : Feasibility Analysis and

t1

t2

  • 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


Design Framework : Feasibility Analysis and

  • “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

t1

t2


Design Framework : Optimization Framework and

  • 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


Design Framework : Optimization Framework and

  • 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(n·h), LPF – O(n)

    • n: # of tasks, h: # of tradeoff values

  • Performance evaluation through simulation


Simulation and

RA by heuristic solution

RA by optimal solution

  • 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 =


Performance of algorithms with 8 tasks
Performance of algorithms with 8 tasks and

Closeness to OPT (%)



Conclusion
Conclusion and

  • 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.


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