LPLB: An approach for the design of a lightweight convolutional neural network

Abstract

SUMMARYConvolutional neural network (CNN) is one of the widely used deep neural network architecture for data analytics in the Internet of Things (IoT). However, due to its severe resource requirements, deploying CNN on resource‐constrained edge devices is quite challenging. Moreover, IoT services demand fast data analytics in order to be useful in their context. Hence, ensuring the deployment of CNN models on IoT edge devices is crucial. To this purpose, this article proposes a framework LPLB (less parameters and less bits) for the design of a lightweight CNN with reduced number of parameters and lesser storage requirements while preserving the model accuracy. LPLB consists of three steps: CNN training, iterative parameter drop and post‐training quantization. The performance of LPLB is evaluated on three datasets: German traffic sign recognition dataset, Kaggle hand gesture recognition dataset, and CIFAR‐10 dataset. Experiments conducted in the study reveal that the proposed framework is able to reduce the number of parameters by 11× in case of German traffic sign recognition dataset, 12.5× in case of Kaggle hand gesture recognition dataset, and 14× in case of CIFAR‐10 dataset. Moreover, the storage requirements are reduced by 44.42× in case of German traffic sign recognition dataset, 50× in case of Kaggle hand gesture recognition dataset, and 56.7× in case of CIFAR‐10 dataset. This is achieved without a significant drop in the accuracy. The accuracy drop in case of German traffic sign recognition dataset is 0.215%, 0.18% in case of Kaggle hand gesture recognition dataset, and 0.148% in case of CIFAR‐10 dataset. The proposed framework is generic and can be used for the design of lightweight CNN models corresponding to other use‐cases as well. This will make real‐time classification/recognition possible in delay‐sensitive applications like self‐driving cars, advanced driver assistance systems, health monitoring, elderly posture recognition and so forth.