Code compression techniques for embedded systems and their effectiveness

Code compression techniques have been used widely in embedded systems to decrease the amount of storage resources needed or to decrease power consumption, and in some cases, to improve performance too. This paper evaluates, using cache models, the performance, power and cost benefits that code compression can provide in an instruction memory hierarchy. It also compares several important code compression schemes on a common platform and using a common set of benchmarks to gauge their effectiveness.

[1]  Wayne H. Wolf,et al.  Code compression for embedded systems , 1998, Proceedings 1998 Design and Automation Conference. 35th DAC. (Cat. No.98CH36175).

[2]  Doug Burger,et al.  Evaluating Future Microprocessors: the SimpleScalar Tool Set , 1996 .

[3]  Trevor Mudge,et al.  MiBench: A free, commercially representative embedded benchmark suite , 2001 .

[4]  Luca Benini,et al.  Selective instruction compression for memory energy reduction in embedded systems , 1999, Proceedings. 1999 International Symposium on Low Power Electronics and Design (Cat. No.99TH8477).

[5]  Nihar R. Mahapatra,et al.  Memory system compression and its benefits , 2002, 15th Annual IEEE International ASIC/SOC Conference.

[6]  Miodrag Potkonjak,et al.  MediaBench: a tool for evaluating and synthesizing multimedia and communications systems , 1997, Proceedings of 30th Annual International Symposium on Microarchitecture.

[7]  Norman P. Jouppi,et al.  Cacti 3. 0: an integrated cache timing, power, and area model , 2001 .

[8]  Nihar R. Mahapatra,et al.  The potential of compression to improve memory system performance, power consumption, and cost , 2003, Conference Proceedings of the 2003 IEEE International Performance, Computing, and Communications Conference, 2003..

[9]  Trevor Mudge,et al.  Efficient execution of compressed programs , 2000 .

[10]  Trevor N. Mudge,et al.  Improving code density using compression techniques , 1997, Proceedings of 30th Annual International Symposium on Microarchitecture.

[11]  Kevin D. Kissell.  MIPS16: High-density MIPS for the Embedded Market1 , 1997 .