How machine learning can expand the Landscape of Edge AI. | TDK – TDK Corporation

Edge AI and the evolution of edge devices

In the context of edge computing, an edge device simply refers to a device that operates at the edges of networks, collecting, processing, and analyzing data. Examples include smartphones, security cameras, smart speakers, and a variety of other devices. In recent years, with the rise of edge AI, these devices have evolved even smarter due to the machine learning functions.

Edge AI*2 is a collective term for technologies related to on-device collection, processing, and analysis of data for artificial intelligence purposes. Commonly, implementing AI requires vast amounts of data and computing power, which is why they are typically run on cloud-based servers. With edge AI, however, data is processed internally on the devices, reducing delays and costs related to data transmission, as well as improving privacy.

Cloud Computing and Edge Computing Compared

The coupling of edge devices with edge AI is broadening the realm of IoT (Internet of Things). Self-driving vehicles, factory automation, and medical device management are examples of edge devices already playing vital roles where real-time data processing and decision-making are required.

Edge AI has traditionally been implemented on devices with robust processing power, such as smartphones and tablets. With the proliferation of IoT, however, interest is growing in a technology known as TinyML (Tiny Machine Learning)*3, which enables small devices with only modest capabilities to execute machine learning functions onboard.

Generally, machine learning is performed on high-performance computers or cloud servers, requiring large amounts of memory and fast processors, incurring commensurate electrical power consumption. This permits the execution of large-scale machine learning models based on vast datasets, resulting in highly accurate image recognition, natural language processing, and more. However, every step of the workflowincluding data collection, model development, and validationusually requires handling by seasoned engineers specialized in each area.

TinyML is a machine learning technology designed for small devices, enabling edge AI to be implemented even on microcontrollers (MCUs), which only possess limited processing muscle. This, in turn, is expected to engender smaller IoT devices with low power consumption. It is now possible to run machine learning inference on almost any device with a sensor and marginal computing power, endowing it with intelligence.

Qeexo, a Silicon Valley startup that joined the TDK Group in 2023, specializes in machine learning solutions for edge devices, with a particular focus on TinyML. Qeexo AutoML, is an end-to-end, no-code (i.e., not requiring code to be hand-written in a programming language) platform that empowers non-engineers to implement machine learning on lightweight edge devices. Working in an intuitive, web-based interface, users can easily perform all the steps necessary to build a machine learning systembeginning with collecting and pre-processing raw data, followed by training and refining recognition models, then finally creating and installing the finished package onto edge devices where the machine learning-based intelligence comes to life.

TDK is currently developing i3 Micro Module, an ultracompact sensor module with onboard edge AI designed to be used for predictive maintenancethe practice of foreseeing and preempting anomalies in machinery and equipment at factories and similar facilities. Sensors, including those for vibration, temperature, and barometric pressure, as well as edge AI and mesh networking capabilities, are all integrated into a compact package, allowing equipment conditions to be monitored without having to rely on manpower, thereby helping minimize downtime and improve productivity. (Photo: Ultracompact sensor module i3 Micro Module)

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Michael A. Gamble, Director, Product Management for Qeexo, explained the significance of Qeexo AutoML. Conventionally, machine learning for embedded devices is a lengthy, complex process requiring highly specialized engineering skills. Qeexo AutoML enables almost anyoneincluding those not technically inclinedto accomplish the same, using an end-to-end, streamlined web interface. Similar to the way digital design tools and audio workstation software opened up graphic arts and music production to just about anyone with a creative spark, AutoML levels the playing field for machine learning. Put simply, we think of Qeexo AutoML as democratizing machine learning.

Advances in edge device technologies have spurred the development of numerous IoT devices and microcontrollers featuring sophisticated machine learning capabilities. With the advent of tools like Qeexo AutoML, it is now possible to create complex machine learning models that run on edge devices in short order.

Letting edge AI process data collected from sensors in edge devices substantially expands the range of possible solutions. Gamble continued, Pairing Qeexos machine learning solutions with TDKs sensor devices will allow us to provide customers with integrated, one-stop solutions. We look forward to a synergistic partnership in developing and delivering smart edge solutions that leverage each others strengths.

Today, edge devices are evolving into intelligent systems that learn by themselves, going well beyond merely gathering and transmitting data. Advanced manufacturing facilities, sometimes referred to as smart factories, will begin equipping almost every piece of machinery and equipment with edge devices. Edge devices are also becoming prevalent among consumers in the form of mobility products and smartphones. Propelled by tools like AutoML, TinyML and edge AI are expected to become increasingly familiar and commonplace. This will all have a significant positive impact on our daily lives, businesses, and industry as a whole.