LIS2DUXS12 datasheet(25/104 Pages) STMICROELECTRONICS

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Finite state machine in the LIS2DUXS12

LIS2DUXS12 accelerometer data can be used as input of up to 8 programs in the embedded finite state machine

(FSM); also the embedded temperature or analog hub (AH) / Qvar sensor data can be processed in FSM logic

(Figure 11. State machine in the LIS2DUXS12).

All 8 finite state machines are independent: each one has its dedicated memory area and it is independently

executed. An interrupt is generated when the end state is reached or when some specific command is performed.

Figure 11. State machine in the LIS2DUXS12

FSM

SIGNAL

CONDITIONING

X = 1..8

Acc [LSB]

Temp/AH/Qvar [LSB]

LIS2DUXS12

FSM output

5.7

Machine learning core

The LIS2DUXS12 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 matches a user-defined set of classes. Typical

examples of applications could be activity detection like running, walking, driving, and so on.

The LIS2DUXS12 machine learning core works on data patterns coming from the accelerometer sensor, but it is

also possible to process the embedded temperature sensor data or the analog hub (AH) / Qvar data.

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 12. Machine learning core in the LIS2DUXS12

Machine

learning core

logical processing

Sensor

data

Computation

block

Decision

tree

Accelerometer

Results

Temperature/AH/Qvar

Features

Filters

Meta-classifier

The LIS2DUXS12 can be configured to run up to 4 decision trees simultaneously and independently and every

decision tree can generate up to 16 results. The total number of nodes can be up to 128.

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

application processor at any time.

The LIS2DUXS12 machine learning core can be configured to generate an interrupt when a change in the result

occurs.

LIS2DUXS12

Machine learning core

DS14035 - Rev 2

page 25/104

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