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Implementation of Random Sparse adaptation using python and tensorflow

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RRAM Simulator Neural Network Training

This is an tensorflow implementation of neural network training and testing for RRAM based hardware accelerators. This is a simulator to emulate RRAM device variation, read/write noise and its effect on neural network inference.

Requirements: software

  1. Requirements for Tensorflow (see: Tensorflow)

  2. Python packages you might not have: cython, easydict

Requirements: hardware

  1. For training, 3G of GPU memory is sufficient (using CUDNN)

Installation

  1. Clone the rramtraining (make sure to clone with --recursive)
git clone --recursive https://[email protected]/amohant4/rramtraining.git