Low Cost Parallel Adaptive Filter Structures

Abstract

In this paper, we present two parallel LMS adaptive filtering algorithms with low hardware. The proposed parallel algorithm 1 doesn't alter the input-output behavior and saves large amount of hardware cost of previous designs, especially when the parallelism level is high. For example, it saves 68.4% of the multiplications and 4.7% of the additions, of those of prior fast parallel adaptive filtering algorithms when parallelism level is 72 and the filter length N is large. The proposed parallel algorithm 2, while maintaining the same performance, can further save 5.56% to 12.5% of the multipliers and 8.54% to 24.9% of the additions when the level of parallelism varies from 3 to 72