COVER STORY
Latest generation of intelligent sensors integrate AI and Machine Learning
What if data computation along with the AI and Machine Learning that is typically carried out in Edge devices today could be integrated into the sensors themselves? Gregorio Vidal, field application engineer at Anglia, introduces the current generation of sensors from STMicroelectronics featuring a Machine Learning Core.
enabling even deeper insight for the applications which employ them. As this more detailed sensor data increases, the need to process it effectively so it can be interpreted correctly becomes ever more important. Devices with onboard sensors are now networked and connected to the IoT allowing the data they gather to be processed in the cloud and interpreted and acted on efficiently. To reduce some of the limitations associated with cloud processing, such as network availability and bandwidth, and to further maximise speed and efficiency, some of this data processing can be carried out locally by “Edge” computing devices. Edge computing helps to increase data privacy, reduce latency and lightens the load on the cloud servers and associated bandwidth.
Growth of ‘Edge’ processing
Sensors have now become pervasive across almost all market segments, from wearable devices which utilise them to monitor users’ movements and vital signs, to sensors attached to industrial machines for Condition Based Monitoring (CbM) to give early warning of wear or failure helping in the drive towards Industry 4.0. The latest generation of MEMS sensors bring ever increasing levels of reliability and measurement accuracy
These Edge devices can also employ Artificial Intelligence (AI) and Machine Learning to further improve performance particularly for time-sensitive applications. For example, in a wearable device measuring a user’s vital signs, it is important to be able to provide a real-time interpretation of the sensor data, whereas in an air quality monitoring device some latency can be tolerated due to the intermittent sampling rates. However, there are also some disadvantages with Edge computing. For example, it requires additional equipment in the form of hubs or router devices to be installed locally further adding cost and complexity.
Sensors with Machine Learning Core STMicroelectronics is addressing these issues by offering integrated computation within its sensors. The company already offers the widest range of sensors covering a full spectrum of applications from low-power devices for IoT
and battery-operated applications to high-end devices for accurate Navigation and Positioning, Industry 4.0, Augmented and Virtual Reality, and Smartphones. They are at the forefront of MEMS sensor development, today their portfolio of sensors covers temperature, humidity, microphone, pressure, proximity, accelerometers, gyroscopes, e-Compasses and multi-axis inertial modules. These sensors have and continue to be refined by leveraging STMicroelectronics’ vast application experience and robust and mature manufacturing processes. STMicroelectronics’ various MEMS sensing elements are manufactured using specialized micromachining processes, while the IC interfaces are developed using CMOS technology that allows the design of a dedicated circuit which is trimmed to better match the characteristics of the sensing element. This enables STMicroelectronics to offer sensors with the lowest power consumption and package sizes in the industry along with excellent stability over the temperature range and low noise which is critical for precision sensing applications. STMicroelectronics’ latest generation of sensors, however, are radically different in that they feature an integrated Machine Learning Core (MLC). This allows the sensor to process data locally within the application device rather than via an external Edge device or in the cloud which is common with current setups. These sensors with integrated computation will not be relevant for every application, however they are of particular interest for time-sensitive applications where the data requires local processing to avoid latency issues associated with remote handling and processing of the data.
8 February 2022
Components in Electronics
www.cieonline.co.uk
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