An efficient video prediction recurrent network using focal loss and decomposed tensor train for imbalance dataset

Mingshuo Liu, Kevin Han, Shiyi Luo, Mingze Pan, Mousam Hossain, Bo Yuan, Ronald F. Demara, Yu Bai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, from companies to academics, researchers across the world are interested in developing recurrent neural networks due to their incredible feats in various applications, such as speech recognition, video detection, predictions, and machine translation. However, the advantages of recurrent neural networks accompanied by high computational and power demands, which are a major design constraint for electronic devices with limited resources used in such network implementations. Optimizing the recurrent neural networks, such as model compression, is crucial to ensure the broad deployment of recurrent neural networks and promote recurrent neural networks for implementing most resource-constrained scenarios. Among many techniques, tensor train (TT) decomposition is considered an up-And-coming technology. Although our previous efforts have achieved 1) expanding limits of many multiplications within eliminating all redundant computations; and 2) decomposing into multi-stage processing to reduce memory traffic, this work still faces some limitations. In particular, current TT decomposition on recurrent neural networks leads to a complex computation sensitive to the quality of training datasets. In this paper, we investigate a new method for TT decomposition on recurrent neural networks for constructing an efficient model within imbalance datasets to overcome this issue. Experimental results show that the proposed new training method can achieve significant improvements in accuracy, precision, recall, F1-score, False Negative Rate (FNR), and False Omission Rate (FOR).

Original languageEnglish (US)
Title of host publicationGLSVLSI 2021 - Proceedings of the 2021 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages391-396
Number of pages6
ISBN (Electronic)9781450383936
DOIs
StatePublished - Jun 22 2021
Event31st Great Lakes Symposium on VLSI, GLSVLSI 2021 - Virtual, Online, United States
Duration: Jun 22 2021Jun 25 2021

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference31st Great Lakes Symposium on VLSI, GLSVLSI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period6/22/216/25/21

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • embedded hardware
  • focal loss
  • tensor decomposition

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