Channel-Adaptive Early Exiting Using Reinforcement Learning for Multivariate Time Series Classification

Pantiskas, Leonardos; Verstoep, Kees; Hoogendoorn, Mark; Bal, Henri

doi:10.1109/icmla58977.2023.00073

2023 International Conference on Machine Learning and Applications (ICMLA) 2023

DOI: 10.1109/icmla58977.2023.00073

|View full text |Cite

Channel-Adaptive Early Exiting Using Reinforcement Learning for Multivariate Time Series Classification

Leonardos Pantiskas,

Kees Verstoep,

Mark Hoogendoorn

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Efficient Recurrent Neural Networks via Importance-Based Sparsification

Ren,

Ni,

et al. 2024

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

Embedded sensing devices that are battery-operated and resource-limited are increasingly utilizing recurrent neural networks (RNNs) to attain real-time perception of the complex environment. Nevertheless, the majority of RNNs typically handle substantial amounts of time-series data, resulting in computational intensity and high energy consumption. Hence, the development of energy-efficient RNN models is a crucial focus. This paper introduces IS-RNN, an energy-efficient RNN model featuring importance-based sparsification, aimed at enhancing the energy efficiency of RNN-based in-sensor inference. The principal motivation for this research arises from the recognition that time series data obtained from sensor sensing often exhibit inherent redundancy. Leveraging input sequence sparsification allows for the elimination of invalid input accesses and computations, thus optimizing efficiency. As a result, optimizing the sparsity of the input sequence through adaptive control allows us to improve inference performance while adhering to energy constraints. In particular, we build an importance score analysis method to quantify the contribution of each input toward the RNN inference performance. Building upon the proposed quantitative importance score analysis, we introduce a dynamic efficient RNN architecture, which enables the model to make decisions based on important data to obtain the highest accuracy under energy constraints. Moreover, we introduce an adaptive RNN inference method to enhance prediction accuracy under energy constraints by making a trade-off between computational cost and model performance. We evaluated IS-RNN on five datasets and the results show that it achieves a higher inference accuracy with the same amount of data processed compared to the state-of-the-art methods.

show abstract

Efficient Recurrent Neural Networks via Importance-Based Sparsification

Ren,

Ni,

et al. 2024

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Channel-Adaptive Early Exiting Using Reinforcement Learning for Multivariate Time Series Classification

Cited by 1 publication

References 29 publications

Efficient Recurrent Neural Networks via Importance-Based Sparsification

Efficient Recurrent Neural Networks via Importance-Based Sparsification

Contact Info

Product

Resources

About