Memristive LSTM architectures

Kazybek Adam, Kamilya Smagulova, Alex James Pappachen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Mainstream standard LSTM architecture that is currently used in Tensorflow library does not use the original architecture. In fact, there are many different architectures of LSTM. One of the more widely used architectures of LSTM is Coupled Input and Forget Gate (CIFG). It is known more as Gated Recurrent Units (GRU). This chapter will introduce the existing architectures of LSTM. Further it will present memristive LSTM architecture implementation in analog hardware. The implementation realizes the standard version of LSTM architecture. Other architecture variations can be easily constructed by rearranging, adding, and deleting the existing analog circuit parts; and adding extra crossbar rows.

Original languageEnglish
Title of host publicationModeling and Optimization in Science and Technologies
PublisherSpringer Verlag
Pages155-167
Number of pages13
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NameModeling and Optimization in Science and Technologies
Volume14
ISSN (Print)2196-7326
ISSN (Electronic)2196-7334

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Analog circuits
Libraries
Hardware
Analog Circuits
Architecture
Analogue
Unit
Standards

ASJC Scopus subject areas

  • Modelling and Simulation
  • Medical Assisting and Transcription
  • Applied Mathematics

Cite this

Adam, K., Smagulova, K., & James Pappachen, A. (2020). Memristive LSTM architectures. In Modeling and Optimization in Science and Technologies (pp. 155-167). (Modeling and Optimization in Science and Technologies; Vol. 14). Springer Verlag. https://doi.org/10.1007/978-3-030-14524-8_12

Memristive LSTM architectures. / Adam, Kazybek; Smagulova, Kamilya; James Pappachen, Alex.

Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. p. 155-167 (Modeling and Optimization in Science and Technologies; Vol. 14).

Research output: Chapter in Book/Report/Conference proceedingChapter

Adam, K, Smagulova, K & James Pappachen, A 2020, Memristive LSTM architectures. in Modeling and Optimization in Science and Technologies. Modeling and Optimization in Science and Technologies, vol. 14, Springer Verlag, pp. 155-167. https://doi.org/10.1007/978-3-030-14524-8_12
Adam K, Smagulova K, James Pappachen A. Memristive LSTM architectures. In Modeling and Optimization in Science and Technologies. Springer Verlag. 2020. p. 155-167. (Modeling and Optimization in Science and Technologies). https://doi.org/10.1007/978-3-030-14524-8_12
Adam, Kazybek ; Smagulova, Kamilya ; James Pappachen, Alex. / Memristive LSTM architectures. Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. pp. 155-167 (Modeling and Optimization in Science and Technologies).
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