# Speeding up Resnet Architecture with Layers Targeted Low Rank Decomposition

Source: [Speeding up Resnet Architecture with Layers Targeted Low Rank Decomposition](<https://www.sota2.com/research/paper/68fa70b01e209131cea4c99d>)

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

Data updated: 2025-10-26T01:47:58.595Z

Authors: Walid Ahmed, Habib Hajimolahoseini, Austin Wen, Yang Liu

Published: 2023-09-21T18:21:54.000Z

## Abstract

Compression of a neural network can help in speeding up both the training and the inference of the network. In this research, we study applying compression using low rank decomposition on network layers. Our research demonstrates that to acquire a speed up, the compression methodology should be aware of the underlying hardware as analysis should be done to choose which layers to compress. The advantage of our approach is demonstrated via a case study of compressing ResNet50 and training on full ImageNet-ILSVRC2012. We tested on two different hardware systems Nvidia V100 and Huawei Ascend910. With hardware targeted compression, results on Ascend910 showed 5.36% training speedup and 15.79% inference speed on Ascend310 with only 1% drop in accuracy compared to the original uncompressed model

## 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/2309.12412>)

[PDF](<https://arxiv.org/pdf/2309.12412.pdf>)