Third Party Tooling and Partner Solutions#

Tooling#

TensorFlow#

The SiSDK supports TensorFlow Lite for Microcontrollers (TFLM) natively. The developer can create a quantized .tflite model file using the TensorFlow environment directly and integrate it into the GDSK.

Getting Started#

• Developing a Model Using TensorFlow and Keras

Silicon Labs Machine Learning Toolkit (MLTK) (Deprecated)#

The Machine Learning Toolkit (MLTK) was created for these reasons to help the Tensorflow developer. It is a set of python scripts designed to follow the typical machine learning workflow for Silicon Labs embedded devices.

Note:

• This package is made available as an open source, self-serve, community supported, reference package with a comprehensive set of online documentation. There are no Silicon Labs support services for this software at this time.

• These scripts are a reference implementations for the use case covered by the documented tutorials. Support for other use cases is the responsibility of the developer.

Getting Started#

• Create a Keyword Spotting Model for an SiSDK example. (GitHub.io) - MLTK Tutorial

Integrating the Model#

The trained model, whether using TensorFlow directly or the MLTK, is represented in a .tflite file. To add the .tflite file to your embedded application, see developing an inference application.

There are several SiSDK examples that include machine learning and show how to run inference using pre-trained models. The pre-trained models can be replaced with a model trained from one of the methods above.

Getting Started#

• Start with any one of these Sample Applications that are provided with the SiSDK.

Partner Toolchains#

These tools curate the end-to-end workflow for creating a machine learning application specific model and accompanying embedded software to include in your application.

They offer:

• An end-to-end coverage of the machine learning workflow

• An easy-to-follow developer experience

• Extra value added features like AutoML, or object detection

Requires:

• Understanding of machine learning concepts (date collection, training, verification, inference, confusion matrix, etc.).

• Ability to collect data that represents the real world scenarios for your product deployment.

The following tools fit well with developing ML as a feature for a Silicon Labs-based embedded application.

Edge Impulse#

Edge Impulse is ushering in the future of embedded machine learning by empowering developers to create and optimize solutions with real-world data. They are making the process of building, deploying, and scaling embedded ML applications easier and faster than ever, unlocking massive value across every industry, with millions of developers making billions of devices smarter.

Getting Started#

Using Edge Impulse Studio with the xG24 Dev Kit

SensiML#

SensiML offers cutting edge AutoML embedded code generation software for implementing AI at the IoT edge. With SensiML Analytics Toolkit, developers have an AI development tool which supports rapid data collection, labeling, feature extraction, ML classification and optimized firmware code generation. Automation built into the tool drastically reduces development time and cost, allowing projects ranging from single users to large teams to generate optimized edge AI sensor algorithms in a fraction of the time that would have otherwise been required with hand coding.

Getting Started#

Using SensiML Analytics Toolkit with the xG24 Dev Kit

Micro.AI#

MicroAI's AtomML is an Edge-native, self-correcting, semi-supervised learning engine that aggregates data from internal device sensors, to tune itself to create a behavioral profile of the asset, which then detects and acts upon abnormal behavior. AtomML brings big infrastructure intelligence into a single piece of equipment or device. Unlike traditional AI-driven asset management solutions that rely on the cloud, AtomML is deployed directly to smart devices and sensors. AtomML operates within the small environment of the device itself, providing a more efficient method for asset analytics and generating real-time alerts. AtomML brings an intimate, local approach to asset management for producing a host of operational efficiencies.

Getting Started#

Using MicroAI AtomML with the xG24 Dev Kit

Partner Solutions#

These tools do not require any knowledge of machine learning to take full advantage of their features. They are designed for specific use cases.

Wake-word/Voice Commands#

Sensory creates a safer and superior UX through vision and voice technologies. Sensory technologies are widely deployed in consumer electronics applications including mobile phones, automotive, wearables, toys, IoT, PCs, medical products, and various home electronics. Sensory’s product line includes TrulyHandsfree voice control, TrulySecure biometric authentication, and TrulyNatural large vocabulary natural language embedded speech recognition. Sensory’s technologies have shipped in over three billion units of leading consumer products.

Getting Started#

Using Sensory Truly Hands Free with the xG24 Dev Kit

Choosing a Machine Learning Tool Based on Use Case#

This section provides getting started links for tools available or suggested, based on use case. The links provided include examples, demos, and tutorials.

Sensor Signal Processing#

Sensor signal processing is the use of low data rate sensors, like accelerometers, gyroscopes, air quality sensors, temperature sensors, pressure sensors, and so on. These use cases can be found in many types of markets, such as preventive maintenance, medical devices, or smoke detectors.

• TensorFlow Magic Wand Example: SiSDK Platform Example

• Predictive Maintenance Tutorial (PDF)

• Air Quality Anomaly Detection Demo: Micro.ai Demo (video 1.5 min)

• BLE PowerPoint Gesture Controller Demo: Neuton Demo (video 3 min)

• BLE PowerPoint Gesture Controller Example: Neuton GitHub example

• Smartwatch Touch Free Control: Neuton Demo (video 1 min)

• Package Condition Monitoring: Neuton Demo (video 2.5 min)

Audio Pattern Matching#

Audio pattern matching uses a microphone to detect different sounds. The types of sounds detected can be a wide range of non-speech related sounds, such as squeaky bearings, jingling keys, breaking glass, water running, animal sounds, and so on.

• Creating an Acoustic Smart Home Door Lock Demo: SensiML Tutorial (5 part blog)

• Introduction to Building an Audio Classifier Demo: Edge Impulse Tutorial Introduction (video 10 min)

• Industrial Predictive Maintenance: Edge Impulse Tutorial (video 70 min), with pre-work instructions (PDF), start on page 12

• Audio classifier Example: MLTK Example

Voice Command#

Voice commands is a specific subset of audio patterns that are the recognition of a small set of spoken words. This is also referred to as keyword spotting. It can be used in a variety of use cases, such as waking up a voice service (i.e., Alexa), or using voice activation for smart home devices or appliances.

• Zigbee Voice Controlled Light Switch Example: SiSDK Zigbee Example

• Voice Controlled LED Example: SiSDK Platform Example

• Create a Voice Command Model for a SiSDK Example: MLTK Tutorial

  • This tutorial can be used for either example above to create a model (i.e., .tflite file).

• Building a Voice Recognition Demo: Edge Impulse Tutorial (video 90 min)

• Voice Command/Wake-word Example: Sensory Truly Hands Free Example

Low-Resolution Vision#

Low resolution vision uses a camera with resolution around 100x100 to detect objects for presence detection, people counting, video wake-up or more.

• People Flow and Counting Example: SiSDK Platform Example

• People Counting Demo: Edge Impulse Example

• Build a Rock, Paper, Scissors Detector Demo: MLTK Tutorial

• Build a Fingerprint Authenticator Demo: MLTK Tutorial

Summary of Machine Learning Tools#

Below is a table that summarizes our different tool options organized by developer skills and target use cases.