Traditionally, IoT edge devices have been perceived primarily as low-power components with limited capabilities for autonomous operations. Yet, with emerging advancements in embedded AI hardware design, a foundational shift paves the way for future possibilities. Thus, the aim of the KDT NEUROKIT2E project is to establish a new open-source framework to further facilitate AI applications on edge devices by developing new methods in quantization, pruning-aware training, and sparsification. These innovations hold the potential to expand the functional range of such devices considerably, enabling them to manage complex Machine Learning (ML) tasks utilizing local resources and laying the groundwork for innovative learning approaches. In the context of 6G's transformative potential, distributed learning among independent agents emerges as a pivotal application, attributed to 6G networks' support for ultra-reliable low-latency communication, enhanced data rates, and advanced edge computing capabilities. Our research focuses on the mechanisms and methodologies that allow edge network-enabled agents to engage in collaborative learning in distributed environments. Particularly, one of the key issues within distributed collaborative learning is determining the degree of confidence in the learning results, considering the spatio-temporal locality of data sets perceived by independent agents.

Alif Semiconductor, supplier of low-power, secure, AI/ML-enhanced fusion processors and microcontrollers (MCUs), announced collaboration with Telit, a global leader in the Internet of Things (IoT), to deliver developer kits that provide cloud-connected hardware and software reference designs for a wide variety of distributed and IoT edge applications. The kits focus on connected AI/ML-enhanced vision, voice, vibration, and sensor applications such as AI cameras, smart home, city infrastructure, biometric access control, and wearables. Ensemble devices utilize innovative aiPM power management technology that feature a High Efficiency, always-on region that senses the environment using initial AI/ML processing, while a separate High Performance region wakes as needed to rapidly execute additional heavy AI/ML workloads and returns to sleep. In addition to smart power management, the Ensemble family provides multiple layers of security based on a secure identity and strong root-of-trust for complete lifecycle management handling keys, certificates, secure boot, remote updates and more. The developer kits include Telit's wireless connectivity modules from Wi-Fi and Bluetooth to LTE and 5G cellular, including low-power wide-area (LPWA) offerings in Cat-M and NB-IoT for IoT applications that require lower power consumption and longer battery life.

We explore Neural Networks (NNs) for keyword spotting (KWS) on IoT devices like smart speakers and wearables. Since we target to execute our NN on a constrained memory and computation footprint, we propose a CNN design that.

This blog post is co-authored by Emmanuel Bertrand, Senior Program Manager, Azure IoT. We recently announced Azure Cognitive Services in containers for Computer Vision, Face, Text Analytics, and Language Understanding. You can read more about Azure Cognitive Services containers in this blog, "Brining AI to the edge." Today, we are happy to announce the support for running Azure Cognitive Services containers for Text Analytics and Language Understanding containers on edge devices with Azure IoT Edge. This means that all your workloads can be run locally where your data is being generated while keeping the simplicity of the cloud to manage them remotely, securely and at scale.

Artificial intelligence is permeating everybody's lives through the face recognition, voice recognition, image analysis and natural language processing capabilities built into their smartphones and consumer appliances. Over the next several years, most new consumer devices will run AI natively, locally and, to an increasing extent, autonomously. But there's a problem: Traditional processors in most mobile devices aren't optimized for AI, which tends to consume a lot of processing, memory, data and battery on these resource-constrained devices. As a result, AI has tended to execute slowly on mobile and "internet of things" endpoints, while draining their batteries rapidly, consuming inordinate wireless bandwidth and exposing sensitive local information as data makes roundtrips in the cloud. That's why mass-market mobile and IoT edge devices are increasingly coming equipped with systems-on-a-chip that are optimized for local AI processing.

The containers can then be deployed to IoT Edge devices. In this session, we provide a scenario about the importance of edge intelligence, and an overview of Azure ML and Azure IoT Edge. Then we demonstrate how to use Azure ML to create a model and run it on an IoT Edge device.

The containers can then be deployed to IoT Edge devices. In this session, we provide a scenario about the importance of edge intelligence, and an overview of Azure ML and Azure IoT Edge. Then we demonstrate how to use Azure ML to create a model and run it on an IoT Edge device.