MEMS Sensors Ecosystem for Machine Learning

Overview
Machine Learning Core
ISPU
Resources
Webinar

The integration of artificial intelligence (AI) algorithms into MEMS sensors is transforming the way that we interact with the world around us. By embedding AI technology at the edge, today's sensors can collect, process, and send meaningful data in real time.

We enable the transition to in-sensor processing with a new generation of smart, open, and accurate sensors to help developers exploit their potential while improving overall system efficiency.

Chip and circuit board

What makes our sensors unique?

  • Smart sensors enable AI at the edge, reducing system data transfer volumes and offloading network processing for lower power consumption and a more sustainable solution
  • An open ecosystem accelerates innovation and product development thanks to data sharing
  • Accurate sensors bring meaningful information to the end users through the development of highly complex algorithms

To ensure developers find the most effective solution in terms of computing capacity and flexibility in programming, ST offers a choice of several technologies for in-sensor processing: sensors with an embedded machine learning core (MLC) and sensors with an intelligent sensor processing unit (ISPU).

MCU processing and in-sensor AI

Sensors with an embedded machine learning core

Machine learning core

An MLC is an engine that can be trained to trigger an action when a specific event is detected using decision tree learning. With an MLC embedded in the sensor, it’s possible to recognize precise movements and communicate the event to a processor with the best possible system energy efficiency.

Added value:

  • Extremely low-power solution
  • Increased accuracy with better context detectability
  • Offloads the main processor, improving system efficiency
Sensor with machine learning core

MEMS sensors with an embedded MLC

Our third-generation of MEMS sensors with advanced machine learning core technology enables intuitive and context-aware functions for the latest battery-operated applications.

Part number Application Family MLC Full scale Temperature range Power consumption Application note for MLC features
LIS2DUX12 Consumer Accelerometer 128 nodes ±16 g -40°C to +85°C 2.7 µA AN5903
LIS2DUXS12 Consumer Accelerometer 128 nodes ±16 g -40°C to +85°C 2.7 µA AN5901
LSM6DSV16X Consumer iNEMO 128 nodes ±4000 dps, ±16 g -40°C to +85°C 0.65 mA combo AN5804
LSM6DSV16BX Consumer iNEMO 128 nodes ±4000 dps, ±16 g -40°C to +85°C 0.95 mA combo AN5892
ASM330LHB Automotive iNEMO 512 nodes ±4000 dps, ±16 g -40°C to +105°C 0.8 mA combo AN5915
ASM330LHHXG1 Automotive iNEMO 512 nodes ±4000 dps, ±16 g -40°C to +125°C 0.8 mA combo AN5987

Other MEMS sensors with an embedded MLC

Part number Application Family MLC Full scale Temperature range Power consumption Application note for MLC features
LSM6DSOX Consumer iNEMO 256 nodes ±2000 dps, ±16 g -40°C to +85°C 0.55 mA combo AN5259
LSM6DSO32X Consumer iNEMO 256 nodes ± 2000 dps; ± 32 g -40°C to +85°C 0.55 mA combo AN5656
LSM6DSRX Consumer iNEMO 512 nodes ±4000 dps, ±16 g -40°C to +85°C 1.2 mA combo AN5393
ISM330DHCX Industrial iNEMO 512 nodes ±4000 dps, ±16 g -40°C to +105°C 1.2 mA combo AN5392
IIS2ICLX Industrial Accelerometer 512 nodes ±3 g -40°C to +105°C 0.42 mA AN5536
ASM330LHHX Automotive iNEMO 512 nodes ±4000 dps, ±16 g -40°C to +105°C 0.8 mA combo AN5781

How to get started with sensors with an embedded MLC?

The best way to get started with machine learning core in sensors is to select the appropriate solution with supporting ST tools and software for your application.

Get started with sensors with an embedded MLC
UNICO

To program the MLC, Unico-GUI is the comprehensive software package for the whole programming flow of the AI algorithm, from collection and labeling to decision tree creation and upload in the sensor.

Sensors with an intelligent sensor processing unit (ISPU)

ISPU

The ISPU is a true integrated digital signal processor (DSP) that is optimized with respect to a general-purpose MCU and can be used to run complex AI algorithms. Its advantage is that - being integrated - it optimizes the required computing power to the maximum.

Added value:

  • Ultra-low power consumption at system level, thanks to optimized data transfer
  • High-processing capability with AI-enabled programmable core (ML and NN)
  • Easily programmable with C language or with commercial and open-source AI models
ISPU integrated in the sensor Asic

MEMS sensors embedding an intelligent sensor processing unit (ISPU)

Part number Application Family Memory Full scale Temperature range Power consumption Application notes for ISPU features
ISM330IS Industrial Inertial Measurement Unit 10 MHz clock, RAM 40 KB ± 2000 dps, ± 16 g -40°C to +85°C 0.59 mA (combo mode) AN5850
ISM330ISN Industrial (anomaly detection) Inertial Measurement Unit 10 MHz clock, RAM 40 KB ± 2000 dps, ± 16 g -40°C to +85°C 0.59 mA (combo mode)
LSM6DSO16IS Consumer Inertial Measurement Unit 10 MHz clock, RAM 40 KB ± 2000 dps, ± 16 g -40°C to +85°C 0.59 mA (combo mode) AN5799

How to get started with sensors with an embedded ISPU?

How to program the ISPU?

There is an AI solution for every need using ISPU.

Get started with sensors with an embedded ISPU

ISPU-Toolchain (C complier)

We provide ISPU programming support with an ecosystem of libraries and third-party tools/IDEs to help you implement even the most complex AI models.

NanoEdge AI Studio

Embedded developers without any data science skills can use NanoEdge AI Studio to program the ISPU (ISM330ISN). You can readily obtain accurate intelligence solutions with a limited amount of time and effort.

Neuton.AI (from ST partner)

No-code TinyML platform enabling everyone regardless of experience or expertise to build and deploy Machine Learning models directly to an ISPU and/or to any MCU natively.

Recommended resources

Ready-to-go application examples in GitHub for AI at the edge

In our GitHub repository you will find application examples both for MLC and ISPU, such as human activity recognition, head gestures, vibration monitoring for predictive maintenance and more. To get started quickly with each example, the README file provides detailed information.

Human activity recognition
Head gestures
Vibration monitoring
Car transportation

Webinars

Event Target
An intelligent sensor for sustainable always-aware applications Machine learning core
In-sensor monitoring with intelligent MEMS sensors Intelligent sensor processing unit
Anyone can build smarter applications with this intelligent IMU Machine learning core
Predictive maintenance with AI at the edge in MEMS sensors Machine learning core
AI for asset tracking using only machine learning core in sensors Machine learning core
Implementing AI in sensors to develop power-efficient personal electronics applications Machine learning core
Program decision tree in sensors with a Machine Learning Core Machine learning core

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