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ESJicient utilization of on-chip memory space is extremely important in modern embedded system applications based on microprocessor cores. In addition to a data cache that interfaces with slower of-chip memory, a fast on-chip SRAM, called Scratch-Pad memory, is often used in several applications. We present a technique for eflciently exploiting onchip Scratch-Pad memory by partitioning the application 's scalar and array variables into off-chip DRAM and on-chip Scratch-Pad SRAM, with the goal of minimizing the total execution time of embedded applications. Our experiments on code kernels from typical applications show that our technique results in signijkant performance improvements.

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Efficient utilization of on-chip memory space is extremely important in modern embedded system applications based on processor cores. In addition to a data cache that interfaces with slower off-chip memory, a fast on-chip SRAM, called Scratch-Pad memory, is often used in several applications, so that critical data can be stored there with a guaranteed fast access time. We present a technique for efficiently exploiting on-chip Scratch-Pad memory by partitioning the application's scalar and arrayed variables into off-chip DRAM and on-chip Scratch-Pad SRAM, with the goal of minimizing the total execution time of embedded applications. We also present extensions of our proposed memory assignment strategy to handle context switching between multiple programs, as well as a generalized memory hierarchy. Our experiments on code kernels from typical applications show that our technique results in significant performance improvements.

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This paper presents a software pipelining algorithm for the automatic extraction of ne-grain parallelism in general loops. The algorithm accounts for machine resource constraints in a way that smoothly integrates the management of resource constraints with software pipelining. Furthermore, generality in the software pipelining algorithm is not sacri ced to handle resource constraints, and scheduling choices are made with truly global information. Proofs of correctness and the results of experiments with an implementation are also presented.