Conversations about why artificial intelligence (AI) and machine learning (ML) are reshaping embedded electronics continue. It is a complex topic for engineering teams and business leaders.

As a subset of AI, machine learning is, perhaps, a little simpler to address. It can deliver technical and commercial benefits, with a lower cost to implement. New value propositions for implementing ML at the edge in industrial, commercial, medical and automotive applications are emerging regularly.

Fundamentally, ML assists in applications where the parameters are well defined. In some ways, ML is the software equivalent of an industrial robot. It has a task, it knows what that task is, and it ignores anything that isn’t related to that task. AI, on the other hand, can be thought of as the autonomous humanoid robot that is free to figure it out for itself.

Even though it may be simpler, implementing ML still involves gathering data, training a model on a large computer, and then porting that model to the target. Often, the target is a smaller processor or microcontroller with a different instruction set from the processor used for training.

The introduction of MEMS sensors with integrated machine learning capability is disrupting this approach to deploying ML. In place of a microcontroller, the sensors have an integrated and dedicated machine learning core. These cores are not programmable in the traditional sense. Engineers don’t need to port the model from one instruction set to another or integrate it into the application code.

Once the machine learning core has been trained to recognize specific activities, the MEMS sensor communicates these detections to a host via user-assigned values in a register. The values correlate to actions and are configured during training. For example, “0” may be assigned to “walking,” while “1” may be assigned to “sitting” for a wearable sensor.

The recognition is based on data coming from the sensing elements and all of the processing takes place in the machine learning core. Although the sensor data is still available to the host processor, it doesn’t need to run an ML model to make sense of the MEMS sensor data.

Even though ML is still an emerging technology, this is a significant deviation from conventional development and deployment. The key to this approach is in the training and configuration of the machine learning core.

Inside a MEMS sensor

To understand how the machine learning core “knows” what the sensors are detecting, let’s look at how a MEMS sensor operates. Inside the sensor is a tiny, machined element. This physical mass is held in place by flexible supports, which allow it to move, or be displaced, by whatever force it is measuring.

In the case of a 3- or 6-axis MEMS sensor, the force may be acceleration or linear displacement, or rotation. The force creates mechanical stress, but it is detected electronically. The stress causes small but measurable changes in the electrical capacitance of the mass. The signal created is equally small, but directly linked to the strength or direction of the force acting upon the mass.

In a regular MEMS sensor, the signal is amplified, digitized and made available to a host processor. The software running on the host will decide what the measurements mean. The OEM’s engineers would need to write the code to evaluate the measurements and resolve them into actions, such as changes in direction or speed of travel.

In STMicroelectronics MEMS sensors equipped with a machine learning core, that evaluation is carried out on the device. Instead of sending separate measurements, the core evaluates all the data into actions or activities.