Dynamically Parameterized Algorithms and Architectures to Exploit Signal Variations

Abstract

Signal processing algorithms and architectures can use dynamic reconfiguration to exploit variations in signal statistics with the objectives of improved performance and reduced power. Parameters provide a simple and formal way to characterize incremental changes to a computation and its computing mechanism. This paper develops a framework for dynamic parameterization and applies it to MPEG-4 motion estimation. A novel motion estimation architecture facilitates the dynamic variation of parameters to achieve power-compression tradeoffs. Our work shows that parameter variation in motion estimation helps achieve power reduction by an order of magnitude, trading off higher compression for lower power. The magnitude of the tradeoffs depends on the input signal variation. The monitoring of input and output signal statistics and subsequent variation of parameters is accomplished by a hardware controller. To provide the controller with a model of the parameter space and corresponding measures in terms of power and performance, a configuration sample space graph is developed. This graph identifies the parameters which present the best power-performance tradeoffs. The controller samples the operating environment to select the appropriate parameters. This reduces the average power consumption by 40% for 2% loss in compression. Four other signal dependent computations: (1) 2D Discrete Cosine Transform, (2) Lempel-Ziv lossless compression, (3) 3D graphics light rendering, and (4) Viterbi decoding are briefly discussed to demonstrate the applicability of dynamic reconfiguration.