Signal Assignment Model for the Memory Management of Multidimensional Signal Processing Applications

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 F. Balasa (B) Dept. of Computer Science, Southern Utah University, Cedar City, UT, USA e-mail: [email protected] I. I. Luican Dept. of Computer Science, University of Illinois at Chicago, Chicago, IL, USA H. Zhu ARM, Inc., Sunnyvale, CA, USA D. V. Nasui American Int. Radio, Inc., Rolling Meadows, IL, USA 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.