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The delay of memory access is one of the major bottlenecks in embedded systems' performance. In software compilation, it is known that there are high variations in memory access delay depending on the ways of storing/retrieving the variables in code to/from the memories. In this paper, we propose effective storage assignment techniques for variables to maximize the use of memory bandwidth. Specifically, we study the problem of DRAM memory layout for storing the nonarray variables in code to achieve a maximum utilization of page and/or burst modes for the memory accesses. The contributions of our work are, for each page and burst modes: 1) we prove that the problem is NP-hard and 2) we propose an exact formulation of the problem and efficient memory layout algorithms, called Solve-MLP for the page mode and Solve-MLB for the burst mode. From experiments with a set of benchmark programs, we confirm that our proposed techniques use on average 28.2% and 10.1% more page accesses and 82.9% and 107% more burst accesses than those by the order of first use and the technique of Panda et al. in Proc. Int. Conf. Computer-Aided Design, 1997, and Panda et al. in ACM Trans. Design Automation Electron. Syst., 1997, respectively.

One of the important issues in embedded system design is to optimize program code for the microprocessor to be stored in ROM. In this paper, we propose an integrated approach to the DSP address-code generation problem for minimizing the number of addressing instructions. Unlike previous works, in which code scheduling and offset assignment are performed sequentially without any interaction between them, our work tightly couples offset assignment problem with code scheduling to exploit scheduling on minimizing addressing instructions more effectively. We accomplish this by developing a fast but accurate two-phase assignment procedure which, for a sequence of code schedules, finds a sequence of memory layouts with minimum addressing instructions. Experimental results with benchmark DSP programs show improvements an average of 5.8% in the whole code size over the existing methods.