Post-compilation optimization for multiple gains with pattern matching
ACM SIGPLAN Notices
Using Lin-Kernighan algorithm for look-up table compression to improve code density
GLSVLSI '06 Proceedings of the 16th ACM Great Lakes symposium on VLSI
Efficient code density through look-up table compression
Proceedings of the conference on Design, automation and test in Europe
Instruction splitting for efficient code compression
Proceedings of the 44th annual Design Automation Conference
Code compression for performance enhancement of variable-length embedded processors
ACM Transactions on Embedded Computing Systems (TECS)
Access pattern-based code compression for memory-constrained systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Huffman-based code compression techniques for embedded processors
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Efficient code compression for embedded processors
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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We present an architecture for compression/decompression of executable files running on embedded systems. Compression is important for memory reduction purposes; previous work on memory reduction for embedded systems has focused on compressing the instruction segment of executable code before execution and decompressing at runtime. Our work has shown that solely compressing the instruction segment is not enough as in many cases executable files contain large data areas that would benefit from compression as well. Compressing data areas presents new challenges to the embedded system designer; data can be modified during execution and therefore a fast compression algorithm and intelligent memory management are required as well. We propose a novel compression/decompression framework that can handle both instructions and data and show memory reductions over 50% while keeping performance degradation within 12%.