Implementation of the Programmable Memristor based – Long Short Term Memory (LSTM) Circuit for Time Series Prediction
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Date
2024-08
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Addis Ababa University
Abstract
Recent advancements in recurrent deep neural networks with Long Short-Term Memory
(LSTM) units have resulted in significant technological advancements in artificial
intelligence. Recurrent Neural Network (RNN) and LSTM analysis of the time-series
data enables us to recognise long-term trends and make predictions that can improve our
way of life. State-of-the-art LSTM models with massively higher ambiguity and a large
number of parameters, on the other hand, have a significant advantage. Previously, they
tried to solve the time series prediction problems using LSTM based architecture, but the
method to solve the problems is more complex, has less accuracy, limited memory capacity,
data communication bandwidth and takes high power consumption. This work
mainly focused on the implementation of already trained LSTM neural network and fully
functional programmable memristor-based LSTM in analog circuitry for the solving time
series prediction with increased the performance and to enhance power consumption of
the system. Using the voltage-based memristive circuit on the LT-spice circuit simulator
and machine learning on python programming with in the combination of the activation
function, and multiplier describe the time series prediction of the LSTM architecture.
Each state will have some noise, and as the noise level increases, so does the prediction accuracy
in analog hardware. As a result we have get results using machine learning based
LSTM and programmable memristor based LSTM of the MSE and RMSE value reduced
by an average of 0.0101 and 0.100 to 0.00915 and 0.095 respectively. We use real-world
regression and prediction problems to demonstrate the capabilities of our system, and
the programmable memristor LSTM is a promising easy-configuration, low-power, and
low-latency hardware platform for edge inference.
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Keywords
LSTM, RNN, Memristor, prediction, Programmable, Crossbar Memristor