LSM6DSTX datasheet(6/175 Pages) STMICROELECTRONICS

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2.4

Machine learning core

The LSM6DSTX embeds a dedicated core for machine learning processing that provides system flexibility,

allowing some algorithms run in the application processor to be moved to the MEMS sensor with the advantage of

consistent reduction in power consumption.

Machine learning core logic allows identifying if a data pattern (for example motion, pressure, temperature,

magnetic data, and so forth) matches a user-defined set of classes. Typical examples of applications could be

activity detection like running, walking, driving, and so forth.

The LSM6DSTX machine learning core works on data patterns coming from the accelerometer and gyroscope

sensors, but it is also possible to connect and process external sensor data (like magnetometer) by using the

sensor hub feature (mode 2).

The input data can be filtered using a dedicated configurable computation block containing filters and features

computed in a fixed time window defined by the user.

Machine learning processing is based on logical processing composed of a series of configurable nodes

characterized by "if-then-else" conditions where the "feature" values are evaluated against defined thresholds.

Figure 3. Machine learning core in the LSM6DSTX

INPUT

Sensor data & hub

Machine

learning core

OUTPUT

Accelerometer

Results

Gyroscope

External Sensor

Features

Filters

Machine

learning core

logical processing

Interrupts

The LSM6DSTX can be configured to run up to 8 flows simultaneously and independently and every flow can

generate up to 16 results. The total number of nodes can be up to 256.

The results of the machine learning processing are available in dedicated output registers readable from the

application processor at any time.

The LSM6DSTX machine learning core can be configured to generate an interrupt when a change in the result

occurs.

LSM6DSTX

Machine learning core

DS14039 - Rev 1

page 6/175

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