LUT Optimization for Distributed Arithmetic-Based Block Least Mean Square Adaptive Filter

摘要

In this paper, we analyze the contents of lookup tables (LUTs) of distributed arithmetic (DA)-based block least mean square (BLMS) adaptive filter (ADF) and based on that we propose intra-iteration LUT sharing to reduce its hardware resources, energy consumption, and iteration period. The proposed LUT optimization scheme offers a saving of 60% LUT content for block size 8 and still higher saving for larger block sizes over the conventional design approach. We also present here the design of a register-based LUT matrix for maximal sharing of LUT contents and full-parallel LUT-update operation. Based on the proposed design approach, we have derived a DA-based architecture for the BLMS ADF, which is scalable for larger block sizes as well as higher filter lengths. We find that the hardware complexity of the proposed structure increases less than proportionately with input block size and filter length. It offers a saving of 60% LUT-update per output and 59% LUT access per output over the recently proposed DA-based BLMS ADF structure for block size 8 and filter length 64. Besides, the proposed structure involves nearly 30% saving in the iteration period over the other for 16-bit coefficient word length. Application specific integrated circuit (ASIC) synthesis result shows that the proposed structure for block size 8 offers a saving of 48% area-delay product (ADP) and 53% energy per sample (EPS) over the existing DA-based BLMS ADF structure on average for different filter lengths, and offers 30% higher sampling rate due to its shorter iteration period. Compared with the existing DA-based LMS ADF structure, the proposed structure involves 68% less ADP and less EPS.