Clustered multi-dictionary code compression method for portable medical electronic systems

Currently, the demand on portable medical electronic systems are increasing, for they provide services and information to both patients and doctors that the traditional medical methods cannot achieve. However, the development of portable medical electronic systems has many limitations, i.e., one has to find a sweet spot among performance, power consumption, size, and cost. For instance, if one only increases the memory of a system, not only does the cost go up, the power consumption also goes up, meanwhile the standby time goes down. In some cases, the hardware size goes up as well. One way of satisfying these constraints while retaining the design and functionality is to compress executable code and data as much as possible. In this paper, a novel clustered multi-dictionary code compression method is proposed to effectively reduce the memory size by replacing the most common codes with shorter codeword. The codes are clustered according to their repeating times. Each cluster is compressed with a different dictionary to make the codeword length different. By pairing clusters and dictionaries with the highest entropy, the compression efficiency becomes the best. Theoretical analysis and experimental results show that this method can achieve significant compression effect. The code of MiBench benchmark compiled under ARM and MIPS instruction set architecture are compressed with this method and the code size decreases by 50%. Aside from high compression ratio, our method also provides relatively fast encoding and very fast decoding.