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Reducing memory requirements of resource-constrained applications
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ACM Transactions on Embedded Computing Systems (TECS) archive
Volume 8 ,  Issue 3  (April 2009) table of contents
Article No. 17  
Year of Publication: 2009
ISSN:1539-9087
Authors
P. Unnikrishnan  IBM Toronto Lab
G. Chen  Microsoft Corporation
M. Kandemir  The Pennsylvania State University
M. Karakoy  Imperial College
I. Kolcu  Univeristy of Manchester
Publisher
ACM  New York, NY, USA
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ABSTRACT

Embedded computing platforms are often resource constrained, requiring great design and implementation attention to memory-power-, and heat-related parameters. An important task for a compiler in such platforms is to simplify the process of developing applications for limited memory devices and resource-constrained clients. Focusing on array-intensive embedded applications to be executed on single CPU-based architectures, this work explores how loop-based compiler optimizations can be used for increasing memory location reuse. Our goal is to transform a given application in such a way that the resulting code has fewer cases (as compared to the original code), where the lifetimes of array elements overlap. The reduction in lifetimes of array elements can then be exploited by reusing memory locations as much as possible. Our experimental results indicate that the proposed strategy reduces data space requirements of 15 resource constrained applications by more than 40%, on average. We also demonstrate how this strategy can be combined with data locality (cache behavior)--enhancing techniques so that a compiler can take advantage of both, that is, reduce data memory requirements and improve data locality at the same time.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Amarasinghe, S. P., Anderson, J. M., Lam, M. S., and Tseng, C. W. 1995. The SUIF compiler for scalable parallel machines. In Proceedings of the 7th SIAM Conference on Parallel Processing for Scientific Computing. Society for Industrial and Applied Mathematics, Philadelphia, PA.
3
4
 
5
 
6
Catthoor, F., Danckaert, K., Kulkarni, C., Brockmeyer, E., Kjeldsberg, P. G., Achteren, T. V., and Omnes, T. 2002. Data Access and Storage Management for Embedded Programmable Processors. Kluwer Academic Publishers, Berlin, Germany.
 
7
8
9
 
10
 
11
 
12
13
14
15
 
16
17
 
18
Kolcu, I. Personal communication.
 
19
Lefebvre V. and Feautrier, P. 1997. Automatic storage management for parallel programs. Res. rep. PRiSM 97/8, France.
 
20
21
22
23
 
24
MediaBench. http://cares.icsl.ucla.edu/MediaBench/.
 
25
MiBench. http://www.eecs.umich.edu/mibench/.
 
26
MIPSpro Family of Compilers. http://www.sgi.com/developers/devtools/languages/mipspro.html.
 
27
28
29
30
 
31
 
32
Unnikrishnan, P., Chen, G., Kandemir, M., Karakoy, M., and Kolcu, I. 2003. Loop transformations for reducing data space requirements of resource-constrained applications. In Proceedings of the 10th Annual International Static Analysis Symposium.
 
33
Verdoolaege, S., Beyls, K., Bruynooghe, M., and Catthoor, F. 2005. Experiences with enumeration of integer projections of parametric polytops. In Proceedings of the 14th International Conference on Compiler Construction. Springer, Berlin, Germany, 91--105.
34
 
35
36
 
37
38
 
39
Zervas, N. D., Masselos, K., and Goutis, C. 1998. Code transformations for embedded multimedia applications: impact on power and performance. In Proceedings of the ISCA Power-Driven Microarchitecture Workshop. ACM, New York.

Collaborative Colleagues:
P. Unnikrishnan: colleagues
G. Chen: colleagues
M. Kandemir: colleagues
M. Karakoy: colleagues
I. Kolcu: colleagues