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.

中文翻译：

---

基于 Clique 的边缘 AI 神经网络分布

分布式智能传感器越来越多地用于生物医学或家庭监控等应用。然而，每个传感器都以无线方式向其他传感器或聚合器广播数据，这导致耗能的传感器节点无法确保数据隐私。为了应对这两个挑战，这项工作建议在每个传感器节点中分配特征提取和基于集团的神经网络 (CBNN) 的一部分。该方案允许在传感器级别标准化数据，确保数据被拦截时的隐私。此外，传输的比特数较少，从而限制了通信开销。基于集团的网络的固有冗余使它们对超出范围的连接具有弹性，从而允许传感器节点的额外功率降低。与聚合器中的局部 CBNN 相比，分布式结构将推理延迟降低了 28%，传感器能耗降低了 25%，并提高了协议的鲁棒性。电路实现可以使用基于单集群迭代集团的电路实现，并针对姿势识别应用进行了演示。为此，制造了一个硬件电路，并在 83ns 内使用每个神经元每个突触事件 115fJ 执行分类。