Exact memory size estimation for array computations without loop unrolling

DAC(1999)

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摘要
This paper presents a new algorithm for exact estimation of the minimum memory size required by programs dealing with array computations. Memory size is an important factor affecting area and power cost of memory units. For programs dealing mostly with array computations, memory cost is a dominant factor in the overall system cost. Thus, exact estimation of memory size required by a program is necessary to provide quantitative information for making high-level design decisions. Based on formulated live variables analysis, our algorithm transforms the minimum memory size estimation into an equivalent problem: integer point counting for intersection/union of mappings of parametrized polytopes. Then, a heuristic was proposed to solve the counting problem. Experimental results show that the algorithm achieves the exactness traditionally associated with totally-unrolling loops while exploiting the reduced computation complexity by preserving original loop structure
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关键词
parallel processing,parametrized polytopes,signal processing,live variables analysis,cache storage,high-level design decisions,minimum memory size,integer point counting,estimation theory,computational complexity,loop structure,exact memory size estimation,memory unit size,array computations,memory unit power cost,exact estimation algorithm,array computation,loop unrolling,semiconductor storage,memory size estimation,counting heuristic,computation complexity reduction,codesign,simulation,design optimization,algorithm design and analysis,digital signal processing,cost function
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