Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this article, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on hierarchical temporal memory that inherently offers online learning, resiliency, and fault tolerance. Architecturally, it is a full custom mixed-signal design with an underlying digital communication scheme and analog computational modules. Therefore, the proposed system features reconfigurability, real-time processing, low power consumption, and low-latency processing. The proposed architecture is benchmarked to predict on real-world streaming data. The network's mean absolute percentage error on the mixed-signal system is 1.129 X lower compared to its baseline algorithm model. This reduction can be attributed to device non-idealities and probabilistic formation of synaptic connections. We demonstrate that the combined effect of Hebbian learning and network sparsity also plays a major role in extending the overall network lifespan. We also illustrate that the system offers 3.46 X reduction in latency and 77.02 X reduction in power consumption when compared to a custom CMOS digital design implemented at the same technology node. By employing specific low power techniques, such as clock gating, we observe 161.37 X reduction in power consumption.
Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.
This research aims to evaluate the trends in the publication of papers on issues related to power circuit breakers, aimed at the technologies implemented, as well as those countries with higher level of publications, making a comparison between authors, research institutes, and a description of the last 10 years with more publications. All the information found in this report is compiled in bibliographic databases such as books of substation designs and research papers with a focus on electrical protection equipment and emphasis on high voltage switching devices. A technology research is conducted over the last ten years to analyze the state of the art of high voltage operating elements with respect to new technologies.
In this paper we review key technological milestones in system embedded optical interconnects in data centers that have been achieved between 2014 and 2020 on major European Union research and development projects. This includes the development of proprietary optically enabled data storage and switch systems and optically enabled data storage and compute subsystems. We report on four optically enabled data center system demonstrators: LightningValley, ThunderValley2, Pegasus and Aurora, which include advanced optical circuits based on polymer waveguides and fibers and proprietary electro-optical connectors. We also report on optically enabled subsystems including Ethernet-connected hard disk drives and microservers. Both are designed in the same pluggable carrier form factor and with embedded optical transceiver and connector interfaces, thus allowing, for the first time, both compute and storage nodes to be optically interchangeable and directly interconnectable over long distances. Finally, we present the Nexus platform, which allows different optically enabled data center test systems and subsystems to be interconnected and comparatively characterized within a data center test environment.
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