LIS2DUX12 datasheet(23/102 Pages) STMICROELECTRONICS

23 / 102 page

Finite state machine in the LIS2DUX12

LIS2DUX12 accelerometer data can be used as input of up to 8 programs in the embedded finite state machine

(FSM). The embedded temperature sensor data can also be processed by FSM logic (Figure 10. State machine

in the LIS2DUX12).

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 10. State machine in the LIS2DUX12

FSM

SIGNAL

CONDITIONING

X = 1..8

Acc [LSB]

Temp [LSB]

LIS2DUX12

FSM output

5.7

Machine learning core

The LIS2DUX12 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 LIS2DUX12 machine learning core works on data patterns coming from the accelerometer sensor, but it is

also possible to process the embedded temperature sensor 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 11. Machine learning core in the LIS2DUX12

Machine

learning core

logical processing

Sensor

data

Computation

block

Decision

tree

Accelerometer

Results

Temperature

Features

Filters

Meta-classifier

The LIS2DUX12 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 LIS2DUX12 machine learning core can be configured to generate an interrupt when a change in the result

occurs.

LIS2DUX12

Machine learning core

DS14071 - Rev 2

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