System-and-ML-Final

Analog Matrix Processors: Unveiling the Power Within

A Comparative Study of Speed and Efficiency Against Digital Processors

Collaborators: Feilong Hou(feilongh@uci.edu) and Jiadong Bai(jiadongb@uci.edu)

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Table of Contents

• System-and-ML-Final
• Analog Matrix Processors: Unveiling the Power Within
  • A Comparative Study of Speed and Efficiency Against Digital Processors
  • Table of Contents
  • Introduction
  • Project Overview
  • Project Structure
  • Requirements
  • Setup

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Introduction

Welcome to our final project for the System and Machine Learning course. In this project, we present a comparative study of Analog Matrix Processors (AMPs) and their speed and efficiency when compared to Digital Processors. We investigate the potential advantages and trade-offs in using AMPs for various computational tasks.

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Project Overview

In this project, we have:

• Conducted a thorough literature review on Analog Matrix Processors and Digital Processors.
• Implemented a range of experiments to evaluate the performance of Analog Matrix Processors.
• Collected and analyzed data to compare the speed and efficiency of AMPs against Digital Processors.
• Presented our findings, insights, and conclusions in a comprehensive report.

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Project Structure

The project is structured as follows:

• code/: Contains all the code used in the experiments and data analysis.
• data/: Includes the datasets used for testing and benchmarking.
• reports/: Contains the final project report, summarizing our findings and results.
• README.md: You are here.

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Requirements

• Python (version 3.7.6)
• NumPy (version 1.20.3)
• Matplotlib (version 3.4.3)
• SciPy (version 1.7.1)
• Pandas (version 1.3.3)

Make sure to install these dependencies before running the code.

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Setup

1. Clone this repository to your local machine:

   git clone https://github.com/FeilongHou/System-and-ML-Final.git