Dynamically parameterized algorithms and architectures to exploit signal variations for improved performance and reduced power

Signal processing algorithms and architectures can use dynamic reconfiguration to exploit variations in signal statistics with the objectives of improved performance and reduced power consumption. Parameters provide a simple and formal way to characterize incremental changes to a computation and its computing mechanism. This paper examines five parameterized computations which are typically implemented in hardware for a wireless multimedia terminal: (1) motion estimation, (2) discrete cosine transform, (3) Lempel-Ziv lossless compression, (4) 3D graphics light rendering and (5) Viterbi decoding. Each computation is examined for the capability of dynamically adapting the algorithm and architecture parameters to variations in their respective input signals. Dynamically reconfigurable low-power implementations of each computation are currently underway