Security is gaining relevance in the development of embedded devices. Toward a secure system at each level of design,
this paper addresses security aspects related to Network-on-Chip (NoC) architectures, foreseen as the communication infrastructure of next-generation embedded devices. In the context of NoC-based multiprocessor systems, we focus on the topic, not yet thoroughly faced, of data protection. In this paper, we present a secure NoC architecture composed of a set of Data Protection Units (DPUs) implemented within the Network Interfaces (NIs). The runtime configuration of the programmable part of the DPUs is managed by a central unit, the Network Security Manager (NSM). The DPU, similar to a firewall, can check and limit the access rights (none, read,write, or both) of processors accessing data and instructions in a shared memory. In particular, the DPU can distinguish between the operating roles (supervisor/user and secure/nonsecure) of the processing elements. We explore alternative implementations of the
DPU and demonstrate how this unit does not affect the network latency if the memory request has the appropriate rights. We also focus on the dynamic updating of the DPUs to support their utilization in dynamic environments and on the utilization of authentication techniques to increase the level of security
Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non-linear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting, by contrasting deep learning architectures on short-term forecast (oneday-ahead prediction). Specifically, the focus is on feedforward and recurrent neural networks, sequence-to-sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one. K E Y W O R D S deep learning, electric load forecasting, multi-step ahead forecasting, smart grid, time-series prediction This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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