Alif Ensemble
The Ensemble series of fusion processors from Alif Semiconductor utilize ARM's low power Cortex-M55 CPUs with dedicated Ethos-U55 microNPUs to run embedded ML workloads quickly and efficiently. The devices feature both 'High Power' cores designed for large model architectures, as well as 'High Efficiency' cores designed for low power continuous monitoring. The Development kit and AppKit are both fully supported by Edge Impulse. The Ensemble kits feature multiple core types, dual MEMS microphones, accelerometers, and a MIPI camera interface.
To get started with the Alif Ensemble and Edge Impulse you'll need the following hardware:
Installing dependencies
To set this device up in Edge Impulse, you will need to install the following software:
The latest
Alif Security Toolkit:
Navigate to the Alif Semiconductor Kit documentation page (you will need to register to create an account with Alif, or log in to your existing Alif account). and download the latest App Security Toolkit (tested with version 0.56.0) for windows or linux.
Extract the archive, and read through the included
Security Toolkit Quick Start Guideto finalize the installation
(Optional) Docker Desktop:
If you are using MacOS, we recommended installing Docker Desktop in order to use the Alif Security Toolkit for programming.
Connecting to Edge Impulse
Once you have installed it's time to connect the development board to Edge Impulse.
1. Configure your hardware
To interface the Alif Ensemble AppKit or Development Kit, you'll need to connect your device to the USB port label PRG USB
2. Flash the default firmware to the device
To program the device adjust jumper J15 to connect pins 1-3 and 2-4
After configuring the hardware, the next step is to flash the default Edge Impulse Firmware. This will allow us to collect data directly from your Ensemble device. To update the firmware:
Download the latest Edge Impulse firmware binary and unzip the file.
Navigate to the directory where you installed the
Alif Security ToolkitCopy the
.binfiles from the Edge Impulse firmware directory into thebuild/imagesdirectory of theAlif Security ToolkitCopy all
.jsonfiles from the Edge Impulse firmware directory into thebuild/configdirectory of theAlif Security Toolkit
Inspect the json files and paths closely to ensure the file names and paths are correctly specified for the binary you intend to flash.
From a command prompt or terminal, run the following commands:
3. Run the Edge Impulse CLI!
To use the serial port of the device adjust jumper J15 to connect pins 1-3 and 2-4
Now, the Ensemble device can connect to the Edge Impulse CLI installed earlier. To test the CLI for the first time, either:
Create a new project from the Edge Impulse project dashboard
OR
Clone an existing Edge Impulse public project, like this Face Detection Demo. Click the link and then press Clone at the top right of the public project.
Then, from a command prompt or terminal on your computer, run:
Device choice
You may see two FTDI or CYPRESS serial ports enumerated for devices. If so, select the second entry in the list, which generally is the serial data connection to the Ensemble device. Ensure that the jumpers are correctly oriented for serial communication.
This will start a wizard which will ask you to log in and choose an Edge Impulse project. You should see your new or cloned project listed on the command line. Use the arrow keys and hit Enter to select your project.
5. Verifying that the device is connected
That's all! Your device is now connected to Edge Impulse. To verify this, go to
Next steps: building a machine learning model
With everything set up you can now build your first machine learning model with these tutorials. This will walk you through the process of collecting data and training a new ML model:
Alternatively, you can test on-device inference with a demo model included in the base firmware binary. To do this, you may run the following command from your terminal:
Then, once you've tested out training and deployment with the Edge Impulse Firmware, learn how to integrate impulses with your own custom Ensemble based application:
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