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In recent years the two trends of edge computing and artificial intelligence became both crucial for information processing infrastructures. While the centralized analysis of massive amounts of data seems to be at odds with computation on the outer edge of distributed systems, we explore the properties of eventually consistent systems and statistics to identify sound formalisms for probabilistic inference on the edge. In particular we treat time itself as a random variable that we incorporate into statistical models through probabilistic programming.
A surge in artificial intelligence and autonomous technologies have increased the demand toward enhanced edge-processing capabilities. Computational complexity and size of state-of-the-art Deep Neural Networks (DNNs) are rising exponentially with div
Computational storage, known as a solution to significantly reduce the latency by moving data-processing down to the data storage, has received wide attention because of its potential to accelerate data-driven devices at the edge. To meet the insatia
As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an exten
The recent advancements of three-dimensional (3D) data acquisition devices have spurred a new breed of applications that rely on point cloud data processing. However, processing a large volume of point cloud data brings a significant workload on reso
Artificial Intelligence (AI) and Internet of Things (IoT) applications are rapidly growing in todays world where they are continuously connected to the internet and process, store and exchange information among the devices and the environment. The cl