# Effective Network Compression Using Simulation-Guided Iterative Pruning

## TL;DR

This paper introduces a simulation-guided iterative pruning method for neural network compression, significantly improving performance at the same pruning levels and enabling deployment in resource-limited systems.

## Contribution

It presents a novel simulation-based iterative pruning approach that enhances network compression effectiveness compared to existing methods.

## Key findings

- Achieved higher performance than existing pruning methods.
- Effective in reducing network size while maintaining accuracy.
- Demonstrated improved compression results through experiments.

## Abstract

Existing high-performance deep learning models require very intensive computing. For this reason, it is difficult to embed a deep learning model into a system with limited resources. In this paper, we propose the novel idea of the network compression as a method to solve this limitation. The principle of this idea is to make iterative pruning more effective and sophisticated by simulating the reduced network. A simple experiment was conducted to evaluate the method; the results showed that the proposed method achieved higher performance than existing methods at the same pruning level.