Low Latency based Floating Point Multiplier using Parallel Prefix Adders

Project Code :TVMAFE729

Objective

1. To study the principles of floating-point arithmetic and the importance of efficient multiplication in high-performance computing and digital signal processing applications. 2. To design a floating-point multiplier architecture that reduces latency by using parallel prefix adders for fast partial product summation.

Abstract

Floating-point multiplication is a critical operation in high-performance computing, digital signal processing, and scientific applications. Traditional floating-point multipliers often face limitations in latency due to sequential addition stages in the mantissa computation. This paper proposes a low-latency floating-point multiplier architecture that employs parallel prefix adders (PPAs) in the mantissa addition stage. By integrating PPAs, the critical path delay is minimized, resulting in faster computation without compromising precision. The proposed design is implemented and evaluated on FPGA platforms, demonstrating significant improvements in speed and throughput compared to conventional methods. This approach enables high-speed arithmetic operations suitable for real-time and energy-efficient 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

Understanding of floating-point arithmetic and multiplier design.

 

Knowledge of different adder architectures (ripple-carry, CLA, PPA) and their impact on latency.

 

Hands-on experience in FPGA-based design and hardware optimization.

 

Ability to analyze trade-offs between speed, area, and power in digital circuits.

 

Insight into implementing high-speed arithmetic operations for DSP and scientific computing applications.

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