Memristive LSTM network hardware architecture for time-series predictive modeling problems

Kazybek Adam, Kamilya Smagulova, Alex James Pappachen

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

3 Citations (Scopus)

Abstract

Analysis of time-series data allows to identify long term trends and make predictions that can help to improve our lives. With rapid development of artificial neural networks, long short-Tern memory (LSTM) recurrent neural network (RNN) configuration is found to be capable in dealing with time-series forecasting problems where data points are time dependent and possess seasonality trends. Gated structure of LSTM cell and flexibility in network topology (one-To-many, many-To-one, etc) allows to model systems with multiple input variables and control several parameters such as the size of look-back window to make a prediction and number of time steps to be predicted. These make LSTM attractive tool over conventional methods such as auto regression models, simple average, moving average, naive approach, ARIMA, Holts linear trend method, Holts Winter seasonal method, and others. In this paper, we propose a hardware implementation of LSTM network architecture for time-series forecasting problem. Circuit simulations were performed in LTspice using TSMC 0.18 μm CMOS technology. Software simulations were performed in Python using Keras library.

Original languageEnglish
Title of host publication2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages459-462
Number of pages4
ISBN (Electronic)9781538682401
DOIs
Publication statusPublished - Jan 8 2019
Event14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018 - Chengdu, China
Duration: Oct 26 2018Oct 30 2018

Publication series

Name2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018

Conference

Conference14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018
CountryChina
CityChengdu
Period10/26/1810/30/18

Keywords

  • analog circuit
  • crossbar
  • LSTM
  • memristor
  • RNN
  • time-series prediction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Instrumentation

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