https://github.com/edgeimpulse/example-standalone-inferencing-ecm3532
Builds and runs an exported impulse locally (Eta Compute ECM3532)
https://github.com/edgeimpulse/example-standalone-inferencing-ecm3532
Last synced: about 1 year ago
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Builds and runs an exported impulse locally (Eta Compute ECM3532)
- Host: GitHub
- URL: https://github.com/edgeimpulse/example-standalone-inferencing-ecm3532
- Owner: edgeimpulse
- License: apache-2.0
- Created: 2020-06-15T13:34:36.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-10-03T21:49:47.000Z (over 2 years ago)
- Last Synced: 2025-02-07T17:15:14.308Z (over 1 year ago)
- Language: C
- Size: 48.3 MB
- Stars: 3
- Watchers: 21
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE-APPLICATION
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README
# Edge Impulse Example: stand-alone inferencing (Eta Compute ECM3532)
This runs an exported impulse on the Eta Compute ECM3532 AI Sensor and AI Vision boards. See the documentation at [Running your impulse locally (ECM3532)](https://docs.edgeimpulse.com/docs/running-your-impulse-ecm3532).
## Requirements
### Hardware
* [Eta Compute ECM3532 AI Sensor](https://etacompute.com/products/) or [Eta Compute ECM3532 AI Vision](https://etacompute.com/products/) development board.
* [Sparkfun FTDI Basic Breakout](https://www.sparkfun.com/products/9873) breakout board, or a similar FTDI to USB board that supports 3.3V - the development boards are not 5V tolerant.
### Software
* [Node.js 12](https://nodejs.org/en/download/) or higher.
* [Python 3](https://www.python.org/download/releases/3.0/).
* [CMake](https://cmake.org) version 3.12.1 or higher.
* [GNU Make](https://www.gnu.org/software/make/).
* [GNU ARM Embedded Toolchain 8-2018-q4-major](https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-rm/downloads) - make sure `arm-none-eabi-gcc` is in your PATH.
* [Edge Impulse CLI](https://docs.edgeimpulse.com/docs/cli-installation).
You can install this via npm:
```
$ npm install edge-impulse-cli@latest -g
```
## Building and flashing the project
1. Deploy your project as a C++ Library and add the *folders* from the export to `Thirdparty/edge_impulse`.
> **Note:** Do not replace the `CMakeLists.txt` file!
1. Build the Edge Impulse firmware:
**AI Sensor board**
```
$ cd Applications/edge-impulse-standalone
$ mkdir build
$ cd build
$ cmake ..
$ make loadconfig CONFIG="../ai-sensor-boot-config"
$ make -j
```
**AI Vision board**
```
$ cd Applications/edge-impulse-standalone
$ mkdir build
$ cd build
$ cmake ..
$ make loadconfig CONFIG="../ai-vision-boot-config"
$ make -j
```
The configuration file you pick, during the `make loadconfig` step depends on the target and flashing method (either with or without bootloader). See Configuration files chapter for more info.
1. To flash your application:
Using the bootloader:
```
$ make flash_bl
```
Using a JLink:
```
$ make flash
```
## Configuration files
The project can build a firmware binary for either the Eta Compute AI Sensor board or the Eta Compute Vision board, depending on the loaded configuration file.
Apart from the different targets, you can also select between between binary for bootloader upload or uploading using a programmer (JLink).
As in the previous paragraph, the config is loaded with the command:
```
$ make loadconfig CONFIG="../ai-sensor-boot-config"
```
The available configurations are:
* ai-sensor-config - AI Sensor without bootloader offset.
* ai-sensor-boot-config - AI Sensor with bootloader offset.
* ai-vision-config - AI Vision without bootloader offset.
* ai-vision-boot-config - AI Vision with bootloader offset.
## License
This repository contains code from the Eta Compute TensaiFlow SDK Alpa v2-0.2, which is listed in [LICENSE-TENSAIFLOW.docx]. The application code is licensed under the Apache 2.0 license (see LICENSE-APPLICATION).