Theory of linear and integer programming
Theory of linear and integer programming
A practical algorithm for exact array dependence analysis
Communications of the ACM
Parallel Computing - Special issue on applications: parallel processing and multimedia
Automatic storage management for parallel programs
Parallel Computing - Special issues on languages and compilers for parallel computers
Optimizing memory usage in the polyhedral model
ACM Transactions on Programming Languages and Systems (TOPLAS)
Reducing memory requirements of nested loops for embedded systems
Proceedings of the 38th annual Design Automation Conference
Custom Memory Management Methodology: Exploration of Memory Organisation for Embedded Multimedia System Design
Storage Size Reduction by In-place Mapping of Arrays
VMCAI '02 Revised Papers from the Third International Workshop on Verification, Model Checking, and Abstract Interpretation
Layer Assignment echniques for Low Energy in Multi-Layered Memory Organisations
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Lattice-Based Memory Allocation
IEEE Transactions on Computers
Memory size computation for multimedia processing applications
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Experiences with enumeration of integer projections of parametric polytopes
CC'05 Proceedings of the 14th international conference on Compiler Construction
SAMOS'09 Proceedings of the 9th international conference on Systems, architectures, modeling and simulation
Enhancing non-linear kernels by an optimized memory hierarchy in a high level synthesis flow
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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The storage requirements of the array-dominated and loop-organized algorithmic specifications running on embedded systems can be significant. Employing a data memory space much larger than needed has negative consequences on the energy consumption, latency, and chip area. Finding an optimized storage of the usually large arrays from these algorithmic specifications is an important step during memory allocation. This paper proposes an efficient algorithm for mapping multi-dimensional arrays to the data memory. Similarly to [13], it computes bounding windows for live elements in the index space of arrays, but this algorithm is several times faster. Moreover, since this algorithm works not only for entire arrays, but also parts of arrays -- like, for instance, array references or, more general, sets of array elements represented by lattices [11], this signal-to-memory mapping technique can be also applied in multi-layer memory hierarchies.