Publication:

Energy-Efficient Recurrent Neural Network Accelerators for Real-Time Inference

Date

Date

Date
2022
Dissertation

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Citation copied

Gao, C. (2022). Energy-Efficient Recurrent Neural Network Accelerators for Real-Time Inference. (Dissertation, University of Zurich) https://doi.org/10.5167/uzh-219686

Abstract

Abstract

Abstract

Over the past decade, Deep Learning (DL) and Deep Neural Network (DNN) have gone through a rapid development. They are now vastly applied to various applications and have profoundly changed the life of hu- man beings. As an essential element of DNN, Recurrent Neural Networks (RNN) are helpful in processing time-sequential data and are widely used in applications such as speech recognition and machine translation. RNNs are difficult to compute because of their massive arithmetic operations and large memory footprint. RNN inference work

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561 since deposited on 2022-08-03
Acq. date: 2025-11-12

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547 since deposited on 2022-08-03
Acq. date: 2025-11-12

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Creators (Authors)

  • Gao, Chang

Institution

Institution

Institution

Faculty

Faculty

Faculty
Faculty of Science

Item Type

Item Type

Item Type
Dissertation

Referees

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Language

Language

Language
English

Publication date

Publication date

Publication date
2022

Date available

Date available

Date available
2022-08-03

Number of pages

Number of pages

Number of pages
213

OA Status

OA Status

OA Status
Green

Metrics

Downloads

561 since deposited on 2022-08-03
Acq. date: 2025-11-12

Views

547 since deposited on 2022-08-03
Acq. date: 2025-11-12

Citations

Citations

Citation copied

Gao, C. (2022). Energy-Efficient Recurrent Neural Network Accelerators for Real-Time Inference. (Dissertation, University of Zurich) https://doi.org/10.5167/uzh-219686

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