In embedded system design, the designer has to choose an onchip memory configuration that is suitable for a specific application. To aid in this design choice, we present a memory exploration strategy based on three performance metrics, namely, cache size, the number of processor cycles and the energy consumption. We show how the performance is affected by cache parameters such as cache size, line size, set associativity and tiling, and the off-chip data organization. We show the importance of including energy in the performance metrics, since an increase in the cache line size, cache size, tiling and set associativity reduces the number of cycles but does not necessarily reduce the energy consumption. These performance metrics help us find the minimum energy cache configuration if time is the hard constraint, or the minimum time cache configuration if energy is the hard constraint.

In this paper we describe a multi-module, multi-port memory design procedure that satisfies area and/or energy constraints for embedded applications. Our procedure consists of application of loop transformations and reordering of array accesses to reduce the memory bandwidth followed by memory allocation and assignment procedures based on ILP models and heuristic-based algorithms. The specific problems include determination of (a) the memory configuration with minimum area, given the energy bound, (b) the memory configuration with minimum energy, given the area bound, (c) array allocation such that the energy consumption is minimum for a given memory configuration (number of modules, size and number of ports per module). The results obtained by the heuristics match well with those obtained by the ILP methods.

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