LSM6DSV320X datasheet(7/212 Pages) STMICROELECTRONICS

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High-g peak tracking feature

The high-g accelerometer peak tracking feature allows monitoring the magnitude of the high-g accelerometer

within a time window that can be defined by issuing dedicated commands from the finite state machine. When

issuing the command related to the end of the monitoring window, this feature stores the high-g accelerometer

three-axis components of the detected peak in the FIFO with a dedicated TAG.

2.6

Machine learning core

The LSM6DSV320X 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 LSM6DSV320X machine learning core works on data patterns coming from the low-g, high-g accelerometer

and gyroscope sensors, but it is also possible to connect and process external sensor data (like magnetometer or

pressure sensor) 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. Computed feature values and filtered data values can also

be read through the FIFO buffer.

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 4. Machine learning core in the LSM6DSV320X

Machine

learning core

logical processing

Sensor

data

Computation

block

Decision

tree

Accelerometer

Results

Gyroscope

External sensor

Features

Filters

Meta-classifier

The LSM6DSV320X can be configured to run up to 8 decision trees simultaneously and independently and every

decision tree 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 LSM6DSV320X machine learning core can be configured to generate an interrupt when a change in the

result occurs.

2.7

Adaptive self-configuration (ASC)

The LSM6DSV320X supports the adaptive self-configuration (ASC) feature, which allows the FSM to

automatically reconfigure the device in real time based on the detection of a specific motion pattern or based on

the output of a specific decision tree configured in the MLC, without any intervention from the host processor. The

FSM can write a subset of the device registers using the SETR command, which allows indicating the register

address and the new value to be written in such a register. The access to these device registers is mutually

exclusive to the host.

LSM6DSV320X

Embedded low-power features

DS14623 - Rev 1

page 7/212

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