Charles Lefurgy

Improving code density using compression techniques

Proposes a method for compressing programs in embedded processors where the instruction memory size dominates the cost. A post-compilation analyzer examines a program and replaces common sequences of instructions with a single instruction codeword. A microprocessor executes the compressed instruction sequences by fetching codewords from the instruction memory, expanding them back to the original sequence of instructions in the decode stage, and issuing them to the execution stages. We apply our technique to the PowerPC, ARM and i386 instruction sets and achieve an average size reduction of 39%, 34% and 26%, respectively, for SPEC CINT95 programs.

Reducing code size with run-time decompression

Compressed representations of programs can be used to improve the code density in embedded systems. Several hardware decompression architectures have been proposed recently. In this paper, we present a method of decompressing programs using software. It relies on using a software-managed instruction cache under control of the decompressor. This is achieved by employing a simple cache management instruction that allows explicit writing into a cache line. We also consider selective compression (determining which procedures in a program should be compressed) and show that selection based on cache miss profiles can substantially outperform the usual execution time based profiles for some benchmarks.

Evaluation of a high performance code compression method

Compressing the instructions of an embedded program is important for cost-sensitive low-power control-oriented embedded computing. A number of compression schemes have been proposed to reduce program size. However, the increased instruction density has an accompanying performance cost because the instructions must be decompressed before execution. In this paper, we investigate the performance penalty of a hardware-managed code compression algorithm recently introduced in IBMs PowerPC 405. This scheme is the first to combine many previously proposed code compression techniques, making it an ideal candidate for study. We find that code compression with appropriate hardware optimizations does not have to incur much performance loss. Furthermore, our studies show this holds for architectures with a wide range of memory configurations and issue widths. Surprisingly, we find that a performance increase over native code is achievable in many situations.