FPGA Implementation of 2D Convolution 4×4 Matrix and 2×2 Kernel Multiplication which using Majority Logic Compressor based Approximate Multiplier

• To design 8-Bit Approximate Multiplier design which using Majority logic based 6:2 Compressor and Exact 4:2 Compressor
• To design the 2D Convolution 4×4 Matrix and 2×2 Kernel Multiplication which using Majority logic Compressor based Approximate Multiplier.

• Modelsim
• Xilinx

Proposed System:

In this work, we propose a novel approach to design a hardware-efficient 2D convolution operation using a 4×4 matrix and a 2×2 kernel multiplication with Majority Logic Compressor (MLC) based Approximate Multiplier. The convolution operation plays a pivotal role in various signal processing and image processing applications, including feature extraction, filtering, and pattern recognition. However, conventional convolution implementations often require significant hardware resources, especially for high-resolution inputs. To address this challenge, we leverage the MLC-based Approximate Multiplier, which offers a trade-off between accuracy and hardware complexity. Our design integrates the MLC-based multiplier into the convolution operation, replacing conventional multipliers to reduce hardware overhead while maintaining reasonable accuracy. We describe the design process, including the specification of the input matrix and kernel, the implementation of the convolution operation, and the design of the MLC-based Approximate Multiplier using Verilog HDL. Furthermore, we discuss testing, evaluation, and optimization strategies to validate the functionality and performance of the proposed design which using Xilinx Spartan-6 FPGA. Our results demonstrate the effectiveness of the proposed approach in achieving hardware-efficient 2D convolution with reduced resource utilization and acceptable accuracy for various applications.

” Thanks for Visit this project Pages – Buy It Soon “

Design and Analysis of a Majority Logic Based Imprecise 6-2 Compressor for Approximate Multipliers