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

1. 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)

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

3. 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%.

4. Code Size vs Execution Time Tradeoff 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

5. Tradeoff Function 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.

6. Tradeoff Function • Linear approximated tradeoff function • we can safely approximate the discrete tradeoff function with a lineartradeoff function. s e

7. Challenging Problem 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.

8. 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.

9. 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.

10. Period Calibration Method (PCM) 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

11. 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

12. Design Framework Overview 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

13. Design Framework : Feasibility Analysis 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

14. 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 t1 t2

15. 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

16. 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(n·h), LPF – O(n) • n: # of tasks, h: # of tradeoff values • Performance evaluation through simulation

17. Simulation 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 =

18. Performance of algorithms with 8 tasks Closeness to OPT (%)

19. Performance of BEST with various task numbers Closeness to OPT (%)

20. 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.