Purpose The recent innovations of Industry 4.0 have made it possible to easily collect data
related to a production environment. In this context, information about industrial equipment …

Artificial intelligence hardware accelerator is an emerging research for several applications
and domains. The hardware accelerator's direction is to provide high computational speed …

Predictive maintenance (PdM) has emerged as a prominent strategy that can recognize the
current state and predict the future trend of machines. It helps prevent disastrous …

Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial
production. With the prevalent deployment of the industrial Internet of Things (IIoTs), an …

This article presents Eciton, a very low-power recurrent neural network accelerator for time
series data within low-power edge sensor nodes, achieving real-time inference with a power …

To process sensor data in the Internet of Things (IoTs), embedded deep learning for 1-
dimensional data is an important technique. In the past, CNNs were frequently used …

Enabling the use of Deep Neural Networks (DNNs) for time-series-based applications on
low-power devices such as wearables opens up a wide range of new features and services …

Unexpected equipment failure is expensive and potentially hazardous for workers and
users. Periodic inspections and maintenance at predefined intervals aim to limit unplanned …

Although neural network quantization is an imperative technology for the computation and
memory efficiency of embedded neural network accelerators, simple post-training …

Deploying Deep Learning (DL) on embedded end devices is a scorching trend in pervasive
computing. Since most Microcontrollers (MCUs) on embedded devices have limited …