Design and Application of Mean and Square Root Circuits for Stochastic Computing

Project Code :TVMAFE776

Objective

The objective of this project is to design and implement mean and square root circuits suitable for stochastic computing systems. It aims to reduce hardware complexity and power consumption while performing approximate arithmetic operations efficiently. The project also seeks to demonstrate these circuits in practical applications such as image processing, signal processing, and probabilistic computing

Abstract

β€”Stochastic computing (SC) is an unconventional computing paradigm that represents values using probabilities. This representation enables simple logic gates to perform complex arithmetic operations. This brief proposes two low hardware-cost stochastic mean circuits for even and odd inputs, respectively, along with a high-accuracy stochastic square root circuit. The circuits are designed by considering correlation technique and achieve excellent performance. Experimental results demonstrate that the proposed mean circuits surpass previous counterparts in computing accuracy and hardware cost. For instance, the proposed 9-input mean circuit can achieve at least an 86.7% reduction in mean square error (MSE) and a 28.6% reduction in area. For the square root circuit, the proposed design achieves a reduction in MSE of at least 19.6%. The proposed circuits are further demonstrated with the Niblack binarization algorithm, which shows superior performance of accuracy.

keywords-Stochastic computing, mean circuit, square root circuit, binarization processing.

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

Block Diagram

Specifications

tools used :

xilinx vivado

Learning Outcomes

  • Understand the principles of stochastic computing and probabilistic arithmetic.

  • Learn to design hardware-efficient mean and square root circuits.

  • Analyze trade-offs between accuracy, power, and hardware complexity in stochastic systems.

  • Gain practical skills in Verilog/VHDL implementation of stochastic arithmetic circuits.

  • Apply stochastic computing techniques to real-time image and signal processing applications.

  • Develop insights into energy-efficient computing for embedded and IoT systems.

  • Demo Video