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Hardware acceleration for recurrent neural networks
Mälardalen University, Sweden.ORCID iD: 0000-0001-5951-9374
Mälardalen University, Sweden.
2020 (English)Book (Other academic)
Abstract [en]

This chapter focuses on the LSTM model and is concerned with the design of a high-performance and energy-efficient solution to implement deep learning inference. The chapter is organized as follows: Section 2.1 introduces Recurrent Neural Networks (RNNs). In this section Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) network models are discussed as special kind of RNNs. Section 2.2 discusses inference acceleration with hardware. In Section 2.3, a survey on various FPGA designs is presented within the context of the results of previous related works and after which Section 2.4 concludes the chapter. 

Place, publisher, year, edition, pages
Institution of Engineering and Technology , 2020.
National Category
Computer and Information Sciences
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URN: urn:nbn:se:ri:diva-67475DOI: 10.1049/PBCS055E_ch2Scopus ID: 2-s2.0-85153645311ISBN: 9781785617683 (print)OAI: oai:DiVA.org:ri-67475DiVA, id: diva2:1802994
Available from: 2023-10-06 Created: 2023-10-06 Last updated: 2023-10-10Bibliographically approved

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Sinaei, Sima

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CiteExportLink to record
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  • apa
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