Distributed Clique-Based Neural Networks for Data Fusion at the Edge

Larras, Benoît; Frappé, Antoine

doi:10.1109/aicas48895.2020.9073801

Cited by 2 publications

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“…The structure of CBNNs [4] allows them either to be embedded as a whole network in the aggregator or to be divided into clusters and dispatched in each node of the WSN. In [5], the latter case, where each cluster is embedded with a memory storing the connections between the neurons, is explored. The circuit structure, as well as the memory organization destined to be embedded is presented.…”

Section: Introductionmentioning

confidence: 99%

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On the Distribution of Clique-Based Neural Networks for Edge AI

Larras

Frappé

2020

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Distributed smart sensors are more and more used in applications such as biomedical or domestic monitoring. However, each sensor broadcasts data wirelessly to the others or to an aggregator, which leads to energy-hungry sensor nodes not ensuring data privacy. To tackle both challenges, this work proposes to distribute the feature extraction and a part of a clique-based neural network (CBNN) in each sensor node. This scheme allows standardizing data at the sensor level, ensuring privacy if the data is intercepted. Besides, a lower number of bits is transmitted, thus limiting the communication overhead. The inherent redundancy of clique-based networks makes them resilient to out-of-range connections, allowing an additional power reduction in the sensor nodes. Compared with a localized CBNN in the aggregator, the distributed structure reduces the inference latency by 28%, the sensor energy consumption by 25% and increases the protocol robustness. The circuit implementation is possible with the use of single-cluster iterative clique-based circuits, and demonstrated for a posture recognition application. To this end, a hardware circuit has been fabricated and performs a classification using 115fJ per synaptic event per neuron in 83ns.

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Section: Introductionmentioning

confidence: 99%

“…The circuit structure, as well as the memory organization destined to be embedded is presented. This paper is an extended version of [5] and includes the following additional contributions:…”

Section: Introductionmentioning

confidence: 99%

On the Distribution of Clique-Based Neural Networks for Edge AI

Larras

Frappé

2020

IEEE J. Emerg. Sel. Topics Circuits Syst.

Self Cite

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A Comprehensive Survey on Collaborative Data-access Enablers in the IIoT

Sun,

Hu,

et al. 2023

ACM Comput. Surv.

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The scope of the Industrial Internet of Things (IIoT) has stretched beyond manufacturing to include energy, healthcare, transportation and all that tomorrow’s smart cities will entail. The realm of IIoT includes smart sensors, actuators, programmable logic controllers, distributed control systems (DCS), embedded devices, supervisory control and data acquisition systems - all produced by manufacturers for different purposes and with different data structures and formats; designed according to different standards, and made to follow different protocols. In this sea of incompatibility, how can we flexibly acquire these heterogeneous data, and how can we uniformly structure them to suit thousand of different applications? In this article, we survey the four pillars of information science that enable collaborative data access in an IIoT - standardization, data acquisition, data fusion, and scalable architecture - to provide an up-to-date audit of current research in the field. Here, standardization in IIoT relies on standards and technologies to make things communicative; data acquisition attempts to transparently collect data through plug-and-play architectures, reconfigurable schemes, or hardware expansion; data fusion refers to the techniques and strategies for overcoming heterogeneity in data formats and sources; and scalable architecture provides basic techniques to support heterogeneous requirements. The paper also concludes with an overview of the frontier researches and emerging technologies for supporting or challenging data access from the aspects of 5G, machine learning, blockchain, and semantic web.

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Distributed Clique-Based Neural Networks for Data Fusion at the Edge

Cited by 2 publications

References 9 publications

On the Distribution of Clique-Based Neural Networks for Edge AI

On the Distribution of Clique-Based Neural Networks for Edge AI

A Comprehensive Survey on Collaborative Data-access Enablers in the IIoT

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