A framework for computation-memory algorithmic optimization for signal processing

The heterogeneity of today's computing environment means computation-intensive signal processing algorithms must be optimized for performance in a machine dependent fashion. In this paper, we present a dynamic memory model and associated optimization framework that finds a machine-dependent, near-optimal implementation of an algorithm by exploiting the computation-memory tradeoff. By optimal, we mean an implementation that has the fastest running time given the specification of the machine memory hierarchy. We discuss two instantiations of the framework: fast IP address lookup, and fast nonuniform scalar quantizer and unstructured vector quantizer encoding. Experiments show that both instantiations outperform techniques that ignore this computation-memory tradeoff.