TY - JOUR
AU - Pandey, Jyoti
AU - Asati, Abhijit R.
PY - 2023
DA - 2023/06/01
TI - Lightweight convolutional neural network architecture implementation using TensorFlow lite
JO - International Journal of Information Technology
SP - 2489
EP - 2498
VL - 15
IS - 5
AB - Recently, with the increase in the precision of convolutional neural networks (CNN) on a wide variety of classification and recognition tasks, the demand for their deployment has dramatically increased. Even the focus is on lightweight, faster, and low-power implementations. In this paper, we have implemented a CNN model onto an embedded platform, ‘Raspberry Pi 4-Model B edge computing system (RP4-BECS)’. This CNN model was initially trained and verified in MATLAB and then implemented on the Machine Learning (ML) framework to generate a TensorFlow lite (TF-lite) flat buffer format. This implementation offers a reduced size of models with good prediction accuracy and lesser inference time as compared with the available literature. We attempted three trials for all the digits from 0 to 9 to evaluate average prediction accuracy and average inference time. An average prediction accuracy of 99.32% and average inference time of 22.53 ms is achieved for the Sign Language Digits Database (SLDD). Further, an average prediction accuracy of 99.09% and average inference time of 13.28 ms is achieved for the Modified National Institute of Standards and Technology Database (MNIST). The model sizes implemented using TF-Lite are highly reduced to 1.53 MB for SLDD and 148 KB for the MNIST database. The obtained accuracy, inference time and model sizes are better than published results.
SN - 2511-2112
UR - https://doi.org/10.1007/s41870-023-01320-9
DO - 10.1007/s41870-023-01320-9
ID - Pandey2023
ER -
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