- 1. Buy Kits
- 2. Create User Account
- 3. 開発環境
- 4. デモを見る
What is AI/ML at Tiny Edge?
In the IoT industry, "edge" refers to devices that perform computation locally instead of relying on cloud computing. The latest development, Tiny Edge, brings computation closer to where data is generated, such as sensor nodes. This shift moves from a centralized, cloud-based solution to a distributed network of edge nodes that collect, process, and infer data locally. By 2027, over 3 billion devices are expected to be sold with TinyML, a subset of AI focused on deploying machine learning models on Tiny Edge devices. This growth is driven by societal trends like the need for speed, privacy and connectivity. Additionally, the transition from wired to wireless technology is further accelerating the adoption of Tiny Edge devices.
Applications of Machine Learning using Silicon Labs’ SoCs
Silicon Labs' Wireless SoCs support a range of ML applications, such as sensor signal processing for predictive and preventative maintenance, bio-signal analysis for healthcare, and cold chain monitoring. They also enable audio pattern matching for security applications, voice commands for smart device control, and low-resolution vision for tasks like people counting and presence detection. The SoCs offer various RAM sizes to accommodate different application requirements. Machine learning models are applied to data from sensors such as microphones, cameras, and those measuring time-series data like acceleration and temperature. These models include audio pattern matching, wake word/command word detection, fingerprint reading, always-on vision, and image/object classification and detection. The detected events can then be further processed according to the requirements.
AI/ML Journey with Silicon Labs
Silicon Labs can accelerate the development of AI/ML devices, starting by outlining each step in the process and helping you along each stage of your project. 私たちは、開発プロセスを簡素化し、デバイスを迅速かつ効率的に市場に投入できるようにお手伝いします。
We have outlined below three key stages of the AI/ML Developer Journey, along with what is required to successfully complete each stage.
開始する
Build Your Own Solution
Pre-Built Solution
1. キットを購入する：ハードウェアと例
Silicon Labs provides offers several development and explorer kits, from ultra-low-cost, small form factor to compact, feature-packed platforms designed for robust networks. We have several exciting demos, including wake-word detection, Pacman, and gesture control. These feature-rich kits support multiple protocols and come in different memory configurations with a wide variety of sensors and peripherals for quick debugging and rapid prototyping. Based on the demos you are interested in, please select the kit that best fits your needs below. The demos are hardware agnostic.
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| キット | EFR32xG24 開発キット | EFR32xG28 エクスプローラー・キット | EFR32xG26 +10 dBm Dev Kit |
SiWx917 Wi-Fi 6 & Bluetooth LE Dev Kit |
| OPN | (xG24-DK2601B） | (xG28-EK2705A) | (xG26-DK2608A) | (SiWx917-DK2605A) |
| サポートされる プロトコル |
Bluetooth, Matter, Proprietary, Thread, Zigbee | Bluetooth, Sidewalk, Wi-SUN, Z-Wave | Bluetooth, Matter, Proprietary, Thread, Zigbee | Bluetooth, Wi-Fi |
| 説明 | EFR32xG24 開発キットは、コンパクトで機能満載の開発プラットフォームです。これは、ワイヤレス IoT 製品の開発とプロトタイプ作成の最速パスを提供します。 | The EFR32xG28 Explorer Kit is small form factor development and evaluation platform based on the EFR32xG28 SoC focused on rapid prototyping and concept creation of IoT applications for Sub-GHz and Bluetooth LE. | EFR32xG26-DK2608A 開発キットは、コンパクトで機能満載の開発プラットフォームです。これは、ワイヤレス IoT 製品の開発とプロトタイプ作成の最速パスを提供します。 | SiWx917 Wi-Fi 6および Bluetooth LE 5.4 開発キットは、ワイヤレス IoT アプリケーションのテスト、開発、プロトタイピングを迅速に行うためのコンパクトでありながら機能満載の開発プラットフォームです。 |
| 価格 | $79 米ドル | $34 米ドル | $89 米ドル | $40 USD *ML enablement in alpha, contact sales |
| Flash/RAM | 1536 kB / 256 kB | 512 kB / 32 kB | 3.2 MB / 512 kB | 8 MB Flash / 8 MB external PSRAM |
| MVP | ✔ | ✔ | ✔ | ✔ |
| センサー | Inertial Sensor, Stereo Microphones, Pressure Sensor, Ambient Light Sensor | 温度センサー | Inertial Sensor, Stereo Microphones, Pressure Sensor, Ambient Light Sensor | Temperature Sensor, Humidity Sensor, Inertial Sensor, Digital Microphone, Ambient Light Sensor |
2. ユーザーアカウントを作成する
開発キットをお待ちの間、ユーザーアカウントを設定することをお勧めします。
Silicon Labs のアカウント：
Silicon Labs のアカウント：このアカウントでは、開発者コミュニティ、入門ガイド、プライベート GitHub リポジトリ、Simplicity Studio 開発環境にアクセスできます。アカウントを作成するか、アカウントへのアクセスを確認することができます。
3. 開発環境の設定
開発環境の選択には多くの選択肢があることは承知していますが、Simplicity Studio は Bluetooth を使用してデバイスを開発するのに最適な選択肢だと考えています。その理由は：
- プログラマとデバッガ機能を搭載しているため、手動セットアップの心配がありません。
- 購入済みのボードを認識し、使用できるサンプルアプリを特定します。
環境の設定にヘルプが必要ですか？スタートガイドをお使いいただければすぐに起動して実行できます。
Simplicity Studio の完全オンライン インストーラーをダウンロードする
システム要件
| Windows | Windows 10（64 ビット） Windows 11 |
| MacOS | 10.14 Mojave 10.15 Catalina* 11.x Big Sur* 12.x Monterey* * Keil 8051 または IAR ツールチェーンご使用する場合は、こちらをクリックしてください |
| Linux | Ubuntu 20.24 LTS |
| CPU | 1 GHz 以上 |
| メモリ | 1 GB RAM（ワイヤレス・プロトコル開発には、8 GB を推奨） |
| ディスク空き容量 | 最低限の FFD インストール用の 600 MB ディスクスペース ワイヤレス・ダイナミックプロトコルのサポートには 7 GB |
4. デモを見る
以下は、最小限のコーディングで簡単に実現できる追加のアイデアのリストです。以下に提案するように、参照されているアプリケーション例を修正します。これらのユースケースは、すぐに使えるデモとしてではなく、さらなる評価のための完璧なコンテキストを提供します。
Voice Control Light
Detects spoken keywords ""on" and "off" to turn on and off LED on board.
Suggested Kit: EFR32xG24 Dev Kit
Get up and running quickly with
pre-built application in 10 minutes.
Learn to create the ML application from
trained model in 30 minutes.
追加デモ
Because starting application development from scratch is difficult, our Simplicity SDK comes with a number of built-in demos and examples covering the most frequent use cases.
Pac-Man
Play the popular Pac-Man game using keywords said out aloud – Go, Left, Right, Up, Down, Stop. The application uses keyword detection. Board can be controlled using Simplicity Studio. The demo is also available as part of Simplicity Studio.
推奨キット：
Audio Classifier
This application uses TensorFlow Lite for Microcontrollers to classify audio data recorded on the microphone in a Micrium OS kernel task. The classification is used to control a LED on the board. The demo is also available as part of Simplicity Studio.
推奨キット：
Magic Wand
This application demonstrates a model trained to recognize various hand gestures with an accelerometer. The detected gestures are printed to the serial port. The demo is also available as part of Simplicity Studio.
推奨キット：
Blink
This application demonstrates a model trained to replicate a sine function. The model is continuously fed with values ranging from 0 to 2pi, and the output of the model is used to control the intensity of an LED. The demo is also available as part of Simplicity Studio.
推奨キット：
1. Build Model
Already have your .tflite file ready to go? Skip to the next step: “Test and Validate” .
Train your model and prepare it for conversion into a deployable format.
If you are familiar with ML development follow these steps –
Customized Code
Begin by designing and training your AI/ML model. This involves gathering and preprocessing data, selecting appropriate model, and setting up training parameters.
To help you build your model from scratch, we provide a Python package with command-line utilities and scripts to assist you with building your own model.
Refer to the TensorFlow documentation for support building on the Machine Learning model. Refer to LiteRT documentation for support on converting the model to .tflite
If you are new to ML development follow these steps –
Low Code
We've partnered with top AI platforms to help you design and build models with minimal coding. These platforms provide user-friendly GUI and automated workflows to simplify the process.
If you are looking for a pre-built Machine Learning solution - jump to the last tab "Pre-Built Solution"
2. Test and Validate
Evaluate your model's performance against the embedded target, validate the model to ensure it meets required performance metrics.
Optional Tool: MLTK Model Profiler
The MLTK model profiler provides information about how efficiently a model may run on an embedded target. The model profiler allows for executing a .tflite model file in a simulator or on a physical embedded target.
Note: This tool is optional and not officially supported by Silicon Labs, yet.
3. Deploy Model
Integrate and deploy your validated model onto the embedded device.
- Add AI/ML SDK Extension
- Configure TensorFlow Micro Component in Studio: set up the component to select the correct kernel for your embedded device
- Include and Run the Model: copy your .tflite model into your application into the config folder in your Simplicity Project
- Implement Post-Processing: add any necessary post-processing steps to handle model’s output and integrate it with your application’s logic
Turn Key Solutions
Pre-built, ready-to-deploy AI/ML solutions on Silicon Labs SoCs that simplify the development process and accelerate time-to-market.
設計パートナー
Silicon Labs has pre-screened and certified the following third-party AI/ML design service companies
to help you design and develop your customized AI/ML solution.
開始する
1. Buy Kits
2. Create User Account
3. 開発環境
4. デモを見る
Build Your Own Solution
1. Build Model
2. Test and Validate
Deploy Model
Pre-Built Solution
パートナー
