Every day more and more software developers are exploring the worlds of machine learning, embedded systems, and the Internet of Things. Perhaps one of the most exciting advances to come out of the most recent innovations in these fields is the incorporation of ML at the edge and into smaller and smaller devices - often referred to as TinyML.
”, , as per Pete’s prediction.
Thousands of developers using TensorFlow can now deploy ML models for actions such as keyphrase detection or gesture recognition onto embedded and IoT devices. However, testing software at scale on many small and embedded devices can still be challenging. Whether it's difficulty sourcing hardware components, incorrectly setting up development environments or running into configuration issues while incorporating multiple unique devices into a multi-node network, sometimes even a seemingly simple task turns out to be complex.
Even experienced embedded developers find themselves trudging through the process of flashing and testing their applications on physical hardware just to accomplish simple test-driven workflows which are now commonplace in other contexts like Web or desktop application development.
The TensorFlow Lite MCU team also faced these challenges: how do you repeatedly and reliably test various demos, models, and scenarios on a variety of hardware without manually re-plugging, re-flashing and waving around a plethora of tiny boards?
To solve these challenges, they turned to that strives to do just that: allow hardware-less, Continuous Integration-driven workflows for embedded and IoT systems.
In this article, we will show you the basics of how to use Renode to run TensorFlow Lite on a virtual RISC-V MCU, without the need for physical hardware (although if you really want to, we’ve also prepared instructions to run the same exact software on a , so this approach can be used with other hardware as well.
What’s the deal with Renode?
At to meet our own needs, but as proud proponents of open source, in 2015 we decided toMake sure you download the proper version for your operating system to have the renode command available. Upon running the renode command in your terminal you should see the Monitor pop up in front of you, which is Renode’s command-line interface.
| . Clone this repository with git (remember to get the submodules): PYTHON |
| As easy as 1-2-3 |
What just happened?
Renode simulates the hardware (both the RISC-V CPU but also the I/O and sensors) so that the binary thinks it’s running on the real board. This is achieved by two Renode features: machine code translation and full SoC support.First, the machine code of the executed application is translated to the native host machine language.
Whenever the application tries to read from or write to any peripheral, the call is intercepted and directed to an appropriate model. Renode models, usually (but not exclusively) written in C# or Python, implement the register interface and aim to be behaviorally consistent with the actual hardware. Thanks to the abstract nature of these models, you can interact with them programmatically from the Renode CLI or from script files.
In our example we feed the virtual sensor with some offline, pre-recorded gesture data files:
PYTHON
i2c.adxl345 FeedSample @circle.dataThe TF Lite binary running in Renode processes the data and - unsurprisingly - detects the gestures.
This shows another benefit of running in simulation - we can be entirely deterministic should we choose to, or devise more randomized test scenarios, feeding specially prepared generated data, choosing different simulation seeds etc.
Building your own application
If you want to build other applications, or change the provided demos, you can now build them yourself using the repository you have downloaded. You will need to install the following prerequisites (tested on Ubuntu 18.04): PYTHON
sudo apt update
sudo apt install cmake ninja-build gperf ccache dfu-util device-tree-compiler wget python python3-pip python3-setuptools python3-tk python3-wheel xz-utils file make gcc gcc-multilib locales tar curl unzip
We will describe how the TensorFlow Lite team uses Renode for Continuous Integration and how you can do that yourself in a separate note soon - stay tuned for that!
Running on hardware
Now that you have the binaries and you’ve seen them work in Renode, let’s see how the same binary behaves on physical hardware.You will need a , connected to the rightmost Pmod connector as in the picture.
| , with a pretty capable RISC-V core and various I/O options. To build the necessary FPGA gateware containing our RISC-V SoC, we will be using . The repository links to tested TensorFlow, Zephyr and LiteX code versions via submodules. Travis CI is used to test the guide. If you’d like to explore more hardware and software with Renode, check the complete list of supported boards. If you encounter problems or have ideas, file an issue on GitHub, and for specific needs, such as enabling TensorFlow Lite and simulation on your platform, you can contact us at [email protected]. Vollständiger Original-Bericht Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org. Wie bewertest du diesen Beitrag? 1 Klick Feedback Teilen mit Netzwerk & Team: Hat Ihnen dieser Tipp / Anleitung geholfen? Community-Analysen & Experten-Meinungen 0Verfasse deine eigene Analyse, teile Workarounds oder diskutiere diesen Vorfall im Blog. Noch keine Community-Analyse verfasst. Markiere einen Textabschnitt oder klicke oben auf „ Eigene Analyse verfassen“! Community Pulse: Relevanz-Einschätzung 1 Klick Experten-Votum 🔴 Akute Relevanz 0% 🟡 In Evaluierung 0% 🟢 Keine Auswirkung 0% Spannende Innovation 0% Verwandte Story-Cluster & Quellen (Vektor-KI) Tipp: Mit Pfeiltasten [ ← ] und [ → ] blättern 
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