1. Selective Code Compression Scheme for Embedded System
Authors:
Shlomit S. Pinter (IBM, Haifa Research Lab)
Israel Waldman (CS Department, Haifa University)
Source: HiPEAC
Presenter: Wen-Long Yang
Date: 2008/05/12

2. Outline Introduction Design of the selective code compression
Experiment results
Conclusion

3. Introduction Code compression Pros Cons
Reduce the storage request of programs
Reduce the access time to off-chip memory
Less traffic between CPU & memory , thus the power consumption will be reduced
Cons
Extra decompression overhead at execution time
May hurt performance

4. Introduction
Code compression is an efficient way to satisfy the resource requirements of the embedded system.
This paper proposes a software-based scheme, which uses profiling information to selectively compress code segments with little run-time overhead.

5. Design 3 main components:
Analysis of input based on profiling information
Select the code segments from the entire program
Preparation of compressed code
Compress the selected code.
Dynamic decompression at execution time
Use decompression engine to decompress the code into a run-time buffer and execute it.

6. Design - analysis Compressible region
Definition: a assembly code fragment that has a single entry and single exit point.
E.g., basic block, procedure calls
Find the maximal compressible regions
use the profiling information(e.g., executed frequency) and the compression algorithm to determine which code segments would be compressed.
Overhead is bound by pre-configured parameters to the compiler
Potential of code size reduction
Estimated slowdown of execution time

7. Design - preparation Compress the selective code into separated files.
Generate the linkage information between compressed codes and other part to the decompression engine
Output a reduced size executable file & a collection of compressed binary units

8. Design – dynamic decompression
Decompress certain compressed binary units into run-time buffer.
Decompression engine retrieve the start address and execute the binary units, and keep the calling sequence of the program.

9. Experiment environments
Processor: 200MHz PowerPC 604
Compiler: IBM xlc v5.0 compiler
Profiling tool: IBM FDPR
Benchmarks:
MediaBench
SPEC CPU2000
181.mcf
177.messa
Because they are likely to be merged into embedded devices.
Parameters:
Reduced code size ≥ 400 bytes
Slowdown of execution time ≤ 2%

10. Experiment results

11. Experiment results
Better cache utilization & less page faults

12. Conclusion
This approach can use profiling for directing code compression without any support of special hardware.
The compression scheme reduces the code size and incurs a reasonable run-time overhead.