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Critical Path Analysis and Low Complexity Implementation of the LMS Adaptive Algorithm
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Critical-Path Analysis and Low-Complexity Implementation of the LMS Adaptive Algorithm

Category : VLSI


Sub Category : AREA EFFICIENT


Project Code : ITVL06


Project Abstract

         This paper presents a precise analysis of the critical path of the least-mean-square (LMS) adaptive filter for deriving its architectures for high-speed and low-complexity implementation. It is shown that the direct-form LMS adaptive filter has nearly the same critical path as its transpose-form counterpart, but   provides much faster convergence and lower register complexity. From the critical-path evaluation, it is further shown that no pipelining is required for implementing a direct-form LMS adaptive filter for most practical cases, and can be realized with a very small adaptation delay in cases where a very high sampling rate is required. Based on these findings, this paper proposes three structures of the LMS adaptive filter: (i) Design 1 having no adaptation delays, (ii) Design 2 with only one adaptation delay, and (iii) Design 3 with two adaptation delays. Design 1 involves the minimum area and the minimum energy per sample (EPS). The best of existing direct-form structures required more area and more EPS compared to Design 1. Designs 2 and 3 involve slightly more EPS than the Design 1 but offer nearly twice and thrice the MUF at a cost of and more area, respectively.

 

EXISTING SYSTEM

PROPOSED  SYSTEM

EXISTING CONCEPT :          

·         The LMS adaptive filter is often believed to have a long critical path due to an inner product computation to obtain the filter output. This is mainly based on the assumption that an arithmetic operation starts only after the complete input operand words are available/generated.

 

PROPOSED CONCEPT :  

·         There are two main computing blocks in the direct-form LMS adaptive filter, namely, i) the error-computation block   and ii) the weight-update block . Most of the area-intensive components are common in the error-computation and weight-update blocks: the multipliers, weight registers, and tapped-delay line.

EXISTING ALGORITHM:

·         LMS adaptive filter.

 

PROPOSED ALGORITHM:

·         Direct-form and transpose-form DLMS adaptive filters.

ALGORITHM DEFENITION: 

·         LMS  algorithms is a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal.

ALGORITHM DEFENITION: 

·         DLMS algorithm is a class of adaptive filter .it is same as least mean square but structure required delay is very low.

DRAWBACKS:

·         Low speed, Low-Throughput.

·         Area high.

ADVANTAGES:

·         Less delay, High speed.

·         Less area requirement.


 
 
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