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Setup CI to build and test machine_learning_applications repo
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‎.github/workflows/ci.yml‎

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name: Continuous Integration for Machine Learning Applications
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on:
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push:
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branches:
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- main
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- feature/*
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tags:
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- v*
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pull_request:
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branches:
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- main
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- feature/*
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workflow_dispatch:
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permissions:
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id-token: write
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contents: read
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jobs:
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generate-matrix:
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runs-on: ubuntu-latest
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outputs:
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matrix: ${{ steps.set-matrix.outputs.matrix }}
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steps:
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- uses: actions/checkout@v5
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- name: Generate dynamic matrix from templates.xml
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id: set-matrix
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run: |
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matrix=$(python3 .github/workflows/scripts/generate_matrix.py | jq -c .)
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echo "matrix=$matrix" >> $GITHUB_OUTPUT
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get-sdk:
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runs-on: ubuntu-latest
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steps:
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- name: Clone GSDK and ai-ml app
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shell: bash
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run: |
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set -e
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mkdir src
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echo "==> Creating developer directories..."
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cd src
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echo "==> Cloning public GSDK"
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git clone https://github.com/SiliconLabs/simplicity_sdk.git gsdk
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cd gsdk
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git checkout v2025.6.2
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mkdir extension
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cd extension
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git clone --recurse-submodules https://github.com/SiliconLabsSoftware/aiml-extension.git aiml-extension
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cd aiml-extension
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git checkout v2.1.2
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git submodule update --init --recursive
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git lfs pull || true
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- name: Checkout machine_learning_applications (this repo)
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uses: actions/checkout@v5
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with:
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path: src/gsdk/extension/machine_learning_applications
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- name: Upload SDK
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uses: actions/upload-artifact@v5
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with:
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name: sisdk-and-extensions
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path: .
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include-hidden-files: true
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get-tools:
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runs-on: ubuntu-latest
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steps:
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- name: Download ARM-GNU and SLC toolchain
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run: |
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mkdir -p tools && cd tools
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wget -q https://developer.arm.com/-/media/Files/downloads/gnu/12.2.rel1/binrel/arm-gnu-toolchain-12.2.rel1-x86_64-arm-none-eabi.tar.xz
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tar -xf arm-gnu-toolchain-12.2.rel1-x86_64-arm-none-eabi.tar.xz
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mv arm-gnu-toolchain-12.2.rel1-x86_64-arm-none-eabi armgnu
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rm arm-gnu-toolchain-12.2.rel1-x86_64-arm-none-eabi.tar.xz
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wget -q https://www.silabs.com/documents/public/software/slc_cli_linux.zip
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unzip -q slc_cli_linux.zip -d slc_cli
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rm slc_cli_linux.zip
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- name: Upload Tools
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uses: actions/upload-artifact@v5
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with:
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name: arm-gnu-toolchain-and-slc
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path: .
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include-hidden-files: true
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build:
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runs-on: ubuntu-latest
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needs: [generate-matrix, get-sdk, get-tools]
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strategy:
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fail-fast: false
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matrix: ${{ fromJSON(needs.generate-matrix.outputs.matrix) }}
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steps:
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- name: Download GSDK
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uses: actions/download-artifact@v5
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with:
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name: sisdk-and-extensions
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path: .
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- name: Download ARM-GNU and SLC toolchain
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uses: actions/download-artifact@v5
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with:
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name: arm-gnu-toolchain-and-slc
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path: .
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- name: Install Java 21
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uses: actions/setup-java@v4
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with:
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distribution: temurin
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java-version: '21'
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check-latest: true
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- name: Configure SLC,ARM-GNU, JAVA paths
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run: |
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set -e
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tree -L 3
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chmod -R +x ${{ github.workspace }}/tools/armgnu
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echo "ARM_GCC_DIR=${{ github.workspace }}/tools/armgnu" >> "$GITHUB_ENV"
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echo "${{ github.workspace }}/tools/armgnu/bin/" >> "$GITHUB_PATH"
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SLC_DIR="${{ github.workspace }}/tools/slc_cli/slc_cli/bin/slc-cli"
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chmod +x "$SLC_DIR/slc-cli"
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ln -sf "$SLC_DIR/slc-cli" "$SLC_DIR/slc"
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echo "UC_CLI_DIR=$SLC_DIR" >> "$GITHUB_ENV"
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echo "$SLC_DIR" >> "$GITHUB_PATH"
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echo "SLC_JAVA_HOME=$JAVA_HOME" >> "$GITHUB_ENV"
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- name: Trust sdk's
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run: |
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set -e
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slc configuration --sdk "${{ github.workspace }}/src/gsdk"
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slc signature trust --sdk "${{ github.workspace }}/src/gsdk"
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slc signature trust --extension-path "${{ github.workspace }}/src/gsdk/extension/aiml-extension"
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slc signature trust --extension-path "${{ github.workspace }}/src/gsdk/extension/machine_learning_applications"
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- name: Generate + Build
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working-directory: ${{ github.workspace }}/src/gsdk/extension/machine_learning_applications
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env:
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APP: ${{ matrix.app }}
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BOARD: ${{ matrix.board }}
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run: |
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set -e
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echo "App: $APP"
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echo "BOARD: $BOARD"
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slc generate -d target/$APP/$BOARD -p $APP.slcp --with $BOARD -s "${{ github.workspace }}/src/gsdk"
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cmake --preset project -S target/$APP/$BOARD/${APP##*/}_cmake
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cmake --build target/$APP/$BOARD/${APP##*/}_cmake/build --parallel
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echo "==> Listing generated .s37 files"
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find ./target -name "*.s37"
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‎.github/workflows/scripts/generate_matrix.py‎

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#!/usr/bin/env python3
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import os, json
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from pathlib import Path
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import xml.etree.ElementTree as ET
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ROOT = Path(__file__).resolve().parents[3]
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TEMPLATES_XML = ROOT / "templates.xml"
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def get_prop(desc, key):
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# <properties key="boardCompatibility" value="...">
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p = desc.find(f'properties[@key="{key}"]')
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return (p.get("value") if p is not None else "").strip()
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def split_ws(s):
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# boardCompatibility is space-separated: "brd2601a brd2601b"
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return [x for x in s.replace(",", " ").split() if x]
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def main():
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tree = ET.parse(TEMPLATES_XML)
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root = tree.getroot()
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rows = []
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for desc in root.findall("descriptors"):
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app = get_prop(desc, "projectFilePaths").split(".")[0] # e.g. application/voice/.../series_2.slcp -> application/voice/.../series_2
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boards = split_ws(get_prop(desc, "boardCompatibility")) # e.g. ["brd2601a", "brd2601b"]
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for board in boards:
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rows.append({
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"app": app,
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"board": board
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})
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if not rows:
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# Avoid empty matrix which makes Actions error out
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rows = [{"app":"noop","board":"noop"}]
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matrix = {"include": rows}
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print(json.dumps(matrix, indent=2))
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if __name__ == "__main__":
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main()

‎templates.xml‎

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<properties key="stockConfigCompatibility" value="com.silabs.ss.framework.project.toolchain.core.default"/>
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<properties key="filters" value="Capability|Machine\ Learning Device\ Type|SoC MCU|32-bit\ MCU Project\ Difficulty|Advanced"/>
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</descriptors>
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<descriptors name="sensory_wakeupword" label="Machine Learning - Sensory Wakeup Word" description="This example project demos wakeup word detection using Sensory's TrulyHandsFree (THF) solution for Series 1 boards. Several models implementing different wake word phrases are included in the provided example projects. You can also create you own wake word model on Sensory's VoiceHub and include it in this demo to try it out.&#xA;">
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<properties key="namespace" value="template.uc"/>
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<properties key="keywords" value="universal\ configurator"/>
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<properties key="projectFilePaths" value="application/voice/sensory_wakeupword/app/sensory_wakeupword_series_1.slcp"/>
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<properties key="readmeFiles" value="application/voice/sensory_wakeupword/README.md"/>
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<properties key="boardCompatibility" value="brd4166a"/>
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<properties key="partCompatibility" value=".*efr32mg12p.*"/>
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<properties key="ideCompatibility" value="iar-embedded-workbench makefile-ide segger-embedded-studio simplicity-ide"/>
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<properties key="toolchainCompatibility" value="gcc iar segger"/>
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<properties key="category" value="Example|Machine Learning"/>
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<properties key="quality" value="EXPERIMENTAL"/>
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<properties key="stockConfigCompatibility" value="com.silabs.ss.framework.project.toolchain.core.default"/>
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<properties key="filters" value="Capability|Machine\ Learning Device\ Type|SoC MCU|32-bit\ MCU Project\ Difficulty|Advanced"/>
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</descriptors>
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<descriptors name="people_flow_counter_mlx90640" label="Machine Learning - People Flow Counter" description="A demo displaying how xG24-DK2601B can be used to to gain information about people flow using an IR camera.&#xA;">
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<properties key="namespace" value="template.uc"/>
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<properties key="keywords" value="universal\ configurator"/>

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