# Structured Model Pruning of Convolutional Networks on Tensor Processing Units

Source: [Structured Model Pruning of Convolutional Networks on Tensor Processing Units](<https://www.sota2.com/research/paper/68fa6a5e1e209131cea27c3f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:08:37.410Z

Authors: Kongtao Chen, Ken Franko, Ruoxin Sang

Published: 2021-07-09T03:41:31.000Z

## Abstract

The deployment of convolutional neural networks is often hindered by high computational and storage requirements. Structured model pruning is a promising approach to alleviate these requirements. Using the VGG-16 model as an example, we measure the accuracy-efficiency trade-off for various structured model pruning methods and datasets (CIFAR-10 and ImageNet) on Tensor Processing Units (TPUs). To measure the actual performance of models, we develop a structured model pruning library for TensorFlow2 to modify models in place (instead of adding mask layers). We show that structured model pruning can significantly improve model memory usage and speed on TPUs without losing accuracy, especially for small datasets (e.g., CIFAR-10).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.04191>)

[PDF](<https://arxiv.org/pdf/2107.04191.pdf>)