In order to effectively apply BranchyNet, a DNN with multiple early-exit branches, in edge
intelligent applications, one way is to divide and distribute the inference task of a …

Edge-based and IoT devices have seen phenomenal growth in recent years, driven by the
surge in demand for emerging applications that leverage machine learning models, such as …

Edge intelligence, as a prospective paradigm for accelerating DNN inference, is mostly
implemented by model partitioning which inevitably incurs the large transmission overhead …

Nowadays, many AI applications utilizing resource-constrained edge devices (eg, small
mobile robots, tiny IoT devices, etc.) require Convolutional Neural Network (CNN) inference …

Deep learning technology has grown significantly in new application scenarios such as
smart cities and driverless vehicles, but its deployment needs to consume a lot of resources …

Abstract Embedding Convolutional Neural Network (CNN) into edge devices for inference is
a very challenging task because such lightweight hardware is not born to handle this …

With the increase of depth and width, Deep Neural Network has got the best results in the
computer vision, but its massive calculation has brought a heavy burden to IOT devices. To …

Internet of Things (IoT) and edge devices have grown in their application fields due to
machine learning (ML) models and their capacity to classify images into previously known …

Deep learning applications have achieved great success in numerous real-world
applications. Deep learning models, especially Convolution Neural Networks (CNN) are …

In promising edge systems, AI algorithms and their hardware implementations are often joint
optimized as integrated solutions to solve end-to-end design problems. Joint optimization …