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Many high-end DSP processors employ both multiple memory banks and heterogeneous register files to improve performance and power consumption. The complexity of such architectures presents a great challenge to compiler design. In this paper, we present an approach for variable partitioning and instruction scheduling to maximally exploit the benefits provided by such architectures. Our approach is built on a novel graph model which strives to capture both performance and power demands. We propose an algorithm to iteratively find the variable partition such that the maximum energy saving is achieved while satisfying the given performance constraint. Experimental results demonstrate the effectiveness of our approach.