Implementation of Adaptive FIR Filter for the Wavelet Transforms Using Distributed Arithmetic Technique

摘要

When computational resources are limited especially multipliers, Distributed Arithmetic (DA) is used in place of the typical multiplier based filtering structures. However, DA is not well suited for the adaptive applications. The bottleneck is updating the memory table. Several attempts have been done to accelerate updating the memory, but at the expense of additional memory usage and of convergence speed. To develop an adaptive DA filter with an uncompromised convergence rate, the memory table must be fully updated. An efficient method of fully updating a DA memory table is involved in this paper. The update method is based on exploiting the temporal locality of the stored data and subexpression sharing. This updation method reduces the computational workload and requires no additional memory resources. The parallelism inherent in DSP may be well exploited to implement the computation intensive discrete wavelet transform and making maximal utilization of the look-up table architecture by reformulating the wavelet computation in accordance with the parallel Distributed Arithmetic algorithm.