Proposed Title :

FPGA Implementation of Runtime Energy Quality Scalable Adaptive Filter using Reversible Wallace Tree Multiplier

Improvement of this project :

• To design Four different adaptive filtering algorithm using two different multipliers and proved the performances of area, delay and power.

• For Existing architecture development, we have used traditional binary multiplier design.

• For Proposed architecture development, we have used Reversible Wallace Tree Multiplier using Toffoli, Feynman, PERES Gate.

• Additionally, Exact resorting divider was integrated in update control block of LMS Adaptive filter for more high performance.

Software Implementation:

• Modelsim
• Xilinx

Proposed System:

ReAdapt is a reconfigurable datapath architecture proposed in this study for scaling the energy quality trade-off of adaptive filtering at runtime. By reorganizing the processing flow in its datapath and limiting the switching activity of unneeded modules using data-gating, the ReAdapt may dynamically pick four adaptive filtering methods for grading complexity levels during runtime. The ReAdapt approach scales the energy-quality trade-off by selecting one of four degrees of filter algorithm complexity: Least mean squares (LMS), partial update normalized LMS (PU-NLMS), set-membership normalized LMS (SM-NLMS), and normalized LMS (NLMS) are the four methods. The ReAdapt design reuses common modules of each adaptive filter, resulting in a VLSI hardware implementation that is compact. In a case study, the ReAdapt architecture operation is evaluated using a 5G input sequence of noise and desired signal. In order to avoid complexity, this work was designed using an accurate resorting divider and coupled with the update control block of the LMS Adaptive filter. The synthesis results of this four distinct level of filter algorithm comparisons with two different multiplier architectures will be demonstrated in Vertex-5 FPGA and outcomes of area, latency, and power will be studied.

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ReAdapt: A Reconfigurable Datapath for Runtime Energy-Quality Scalable Adaptive Filters