The biggest growth rate of network traffic in the coming years will be for smartphones and Internet-connected devices, which relentless tend to perform increasingly demanding tasks on continuously increasing amounts of data. Machine Learning and Edge Computing are emerging as effective paradigms for processing huge amounts of data supplied by the Internet of Things and Smart Cities. An osmotic computing architecture for an IoT smart classroom is used for testing a deep learning model for person recognition. A comparative performance study and analysis was made by means of selecting a single deep learning model, that it was tried to be adapted to run over the cloud, a fog microserver and a mobile edge computing device. The results obtained shown some promising results and also limitations for the edge and fog computing side that will need to be addressed in order to minimize latencies and achieve real-time responses for the present IoT application.
With the rapid proliferation of Internet of Things and intelligent edge devices, there is an increasing need for implementing machine learning algorithms, including deep learning, on resource-constrained mobile embedded devices with limited memory and computation power. Typical large Convolutional Neural Networks (CNNs) need large amounts of memory and computational power, and cannot be deployed on embedded devices efficiently. We present Two-Bit Networks (TBNs) for model compression of CNNs with edge weights constrained to (-2, -1, 1, 2), which can be encoded with two bits. Our approach can reduce the memory usage and improve computational efficiency significantly while achieving good performance in terms of classification accuracy, thus representing a reasonable tradeoff between model size and performance.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.