Speed Read

This is the github page for the results and code to reproduce the results for:

1. Counting Cards: Exploiting Weight and Variance Distributions for Robust Compute In-Memory
2. Breaking Barriers: Maximizing Array Utilization for Compute In-Memory Fabrics

This tool has evolved to support different readout algorithms and system level allocation policies.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

What things you need to install the software and how to install them

Python 3
gcc
g++

Installing

git clone https://github.com/bcrafton/speed_read

Running

cd speed_read/
python tb.py

Hardware

I run parallel tests with 8 threads, but this can be configured for more if you have them.

Authors

• Brian Crafton

Affiliation

Georgia Institute of Technology, ICSRL (http://icsrl.ece.gatech.edu/)

License

This project is licensed under the MIT License - see the LICENSE.md file for details