IEEE Systems, Man and Cybernetics Magazine - April 2022 - 43

taking 70.5% less time to train the model. Moreover, the
accuracy of the TPU trained model was adequate to be
employed in a real-life application, which makes the
tradeoff between training time and accuracy profitable.
This means that the TPU is a more efficient option for
training and testing different machine learning-based
Bitcoin price-prediction models.
Conclusion
As the craze for cryptocurrency rages, better and more
efficient methods to predict the price of Bitcoin will be
needed. In this article, we have developed an LSTM-based
Bitcoin price-prediction model using univariate time-series
forecasting. Furthermore, we have parallelized the model
using a TPU to reduce the training time. The parallelized
model has shown an ability to reduce the training time by a
significant percentage without adversely affecting the accuracy
of the model. In addition, the parallelization of the
model did not require any significant changes in the code.
However, the proposed method employed only one machine
learning algorithm: LSTM. Moreover, only two parameters
were used to evaluate the performance of the sequential
and distributed models. In the future, multivariate neural
networks could be utilized to develop more complex Bitcoin
price-prediction models. Additionally, a larger number of
evaluation parameters could be used to gain further insight
into the performance and efficiency of parallelized models.
This could help provide a better understanding of the usability
and efficiency of distributed machine learning and specialized
processors, such as TPUs.
About the Authors
Mohd Hammad Khan (mohdhammad.khan2018@
vitstudent.ac.in) is a student in the School of Computer
Science and Engineering, Vellore Institute of Technology,
Vellore 632 014, Tamil Nadu, India.
Devdutt Sharma (devdutt.sharma2018@vitstudent.ac
.in) is a student in the School of Computer Science and
Engineering, Vellore Institute of Technology, Vellore 632 014,
Tamil Nadu, India.
N. Narayanan Prasanth (nnprcd@gmail.com) is an
associate professor in the School of Computer Science and
Engineering, Vellore Institute of Technology, Vellore 632 014,
Tamil Nadu, India.
S.P. Raja (avemariaraja@gmail.com) is an associate professor
of School of Computer Science and Engineering, Vellore
Institute of Technology, Vellore 632 014, Tamil Nadu, India.
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April 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 43

https://cloud.google.com/tpu/docs/concepts https://www.fxcm.com/markets/insights/what-causes-volatility-in-bitcoin/ https://www.fxcm.com/markets/insights/what-causes-volatility-in-bitcoin/ https://www.quandl.com/data/BCHAIN/MKPRU-Bitcoin-Market-Price-USD https://www.quandl.com/data/BCHAIN/MKPRU-Bitcoin-Market-Price-USD https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu https://www.tensorflow.org/api_docs/python/tf/distribute https://www.tensorflow.org/api_docs/python/tf/distribute

IEEE Systems, Man and Cybernetics Magazine - April 2022

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