We studied the architecture of embedded computing systems from the viewpoint of power consumption in memory systems and used a selective-code-compression (SCC) approach to realize our design. Based on the LZW (Lempel-Ziv-Welch) compression algorithm

we propose a novel cost effective compression and decompression method. The goal of our study was to develop a new SCC approach with an extended decision policy based on the prediction of power consumption. Our decompression method had to be easily implemented in hardware and to collaborate with the embedded processor. The hardware implementation of our decompression engine uses the TSMC 0.18 μm-2p6m model and its cell-based libraries. To calculate power consumption more accurately

we used a static analysis method to estimate the power overhead of the decompression engine. We also used variable sized branch blocks and considered several features of very long instruction word (VLIW) processors for our compression

including the instruction level parallelism (ILP) technique and the scheduling of instructions. Our code-compression methods are not limited to VLIW machines

and can be applied to other kinds of reduced instruction set computer (RISC) architecture.