Theory of linear and integer programming
Theory of linear and integer programming
Faster scaling algorithms for general graph matching problems
Journal of the ACM (JACM)
A singular value decomposition updating algorithm for subspace tracking
SIAM Journal on Matrix Analysis and Applications
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)
Data and memory optimization techniques for embedded systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Storage Management Programmable Process
Storage Management Programmable Process
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
Implementation of algorithms for maximum matching on nonbipartite graphs.
Implementation of algorithms for maximum matching on nonbipartite graphs.
Lattice-Based Memory Allocation
IEEE Transactions on Computers
Signal-to-Memory Mapping Analysis for Multimedia Signal Processing
ASP-DAC '07 Proceedings of the 2007 Asia and South Pacific Design Automation Conference
Computation of storage requirements for multi-dimensional signal processing applications
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Address Generation Optimization for Embedded High-Performance Processors: A Survey
Journal of Signal Processing Systems
Experiences with enumeration of integer projections of parametric polytopes
CC'05 Proceedings of the 14th international conference on Compiler Construction
Data dependency size estimation for use in memory optimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Many signal processing systems, particularly in the multimedia and telecom domains, are synthesized to execute data-dominated applications. Their behavior is described in a high-level programming language, where the code is typically organized in sequences of loop nests and the main data structures are multidimensional arrays. Since data transfer and storage have a significant impact on both the system performance and the major cost parameters--power consumption and chip area, the designer must spend a significant effort during the system development process on the exploration of the memory subsystem in order to achieve a cost-optimized design. This paper presents a memory allocation methodology for multidimensional signal processing applications, focusing on the problem of efficiently mapping the multidimensional signals from the algorithmic specification into the physical memory. In a first phase, two previous mapping models are implemented within a common theoretical framework, which is advantageous from both the point of view of computational efficiency and the amount of allocated data storage. Different from all the previous mapping models that aim to optimize the memory sharing between the elements of a same array (creating separate windows in the physical memory for distinct arrays), this proposed mapping model exploit--in a second phase--the possibility of memory sharing between the elements of different arrays. As a consequence, this signal assignment approach yields significant savings in the amount of data storage resulted after mapping.