VLSI design of 128-point finite field FFT multiplier

Also Available Domains Arithmetic Core

Project Code :TVMAFE726

Objective

1. To study the principles of finite field arithmetic and its application in digital signal processing, particularly in Galois fields (GF) for FFT computations. 2. To design a 128-point Fast Fourier Transform (FFT) architecture optimized for finite field multiplication in hardware.

Abstract

Finite field arithmetic plays a crucial role in modern digital signal processing, error control coding, and cryptographic systems. Fast Fourier Transform (FFT) over finite fields, also known as Number Theoretic Transform (NTT), is widely used to accelerate polynomial multiplication and convolution operations. This project presents the VLSI design of a 128-point finite field FFT multiplier optimized for high speed and low power consumption. The proposed architecture exploits parallelism inherent in FFT algorithms while employing efficient modular multiplication and addition units suitable for finite field operations. By carefully designing butterfly units, modular arithmetic blocks, and pipeline stages, the multiplier achieves reduced computational latency and improved throughput. The design is well suited for ASIC and FPGA implementations in high-performance and security-critical applications.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

  • Software Requirements::
  • Xilinx Vivado Design Suite (2020.2 or later) 
  • Verilog HDL for RTL design and implementation
  • Vivado Simulator (XSIM) for functional and timing verification
  • MATLAB (optional) for GPR data preprocessing, noise modeling, and result validation

Hardware Requirements

  • Microsoft® Windows 10 / Windows 11 (64-bit)
  • Intel® Core™ i5 / i7 Processor or equivalent
  • Minimum 8 GB RAM
Minimum 500 MB free disk space

Learning Outcomes

Understand finite field arithmetic and FFT principles

 

Gain knowledge of VLSI-based FFT architecture design

 

Learn optimization techniques for modular multipliers

 

Analyze trade-offs between speed, area, and power

 

Develop practical skills in designing DSP accelerators for cryptographic applications

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