# (Pen-) Ultimate DNN Pruning

## TL;DR

This paper introduces a novel DNN pruning method using PCA and neuron importance that automatically optimizes network size in a single step, avoiding extensive retraining and hyperparameter tuning.

## Contribution

It presents a one-shot pruning scheme based on PCA and neuron importance, eliminating the need for iterative retraining and manual hyperparameter tuning.

## Key findings

- Reduces DNN size without accuracy loss
- Eliminates multiple retraining cycles
- Automatically finds optimized network configuration

## Abstract

DNN pruning reduces memory footprint and computational work of DNN-based solutions to improve performance and energy-efficiency. An effective pruning scheme should be able to systematically remove connections and/or neurons that are unnecessary or redundant, reducing the DNN size without any loss in accuracy. In this paper we show that prior pruning schemes require an extremely time-consuming iterative process that requires retraining the DNN many times to tune the pruning hyperparameters. We propose a DNN pruning scheme based on Principal Component Analysis and relative importance of each neuron's connection that automatically finds the optimized DNN in one shot without requiring hand-tuning of multiple parameters.