The example applications described here have been developed to run on the
[Coralboard SL](https://developers.google.com/coral/products), using the [Synaptics Torq NPU
compiler](https://synaptics-torq.github.io/torq-compiler/v/latest/). All of the
source code is available from the [Synaptics Coralboard repository
here](https://github.com/synaptics-astra-demos/sl2610-examples), and a link to
the GitHub Readme is given below for each example.
If are a new user of the Coralboard SL, see the [Getting
started](https://developer.synaptics.com/docs/developer-kits/coralboard/sl/get-started)
page on the Synaptics' site.
## Voice actions
### Use natural language to invoke commands on real-world physical systems.
A Gemma LLM model runs fully on-device, mapping the intent from human language
input and converting it to APIs that control LEDs and a buzzer on the Synaptics
Coralboard. For example, "change the color to *color* ", "make it blink *n*
times", "beep *n* times". Achieved through a hybrid, multimodal architecture
integrating MoonshineAI's Speech-to-Text model running on the Torq-Coral NPU,
and Google FunctionGemma 270M running on the dual-core Arm CPU. The Gemma model
can be fine-tuned to a specific vocabulary.
See the
[Readme](https://github.com/synaptics-astra-demos/sl2610-examples/blob/main/Function_calling/README.md)
for more details.
## Language translation
### Use on-device translation to translate spoken sentences between languages.
A speech-to-text transformer captures the spoken words, encoding them into
tokens that are passed into Gemma LLM for translation. This demo supports
translation from English to multiple languages, including Spanish and French.
Additional languages can be supported with fine tuning.
This application is achieved through a hybrid, multimodal architecture
integrating MoonshineAI's speech-to-text model and Google's Gemma3-270M for
translation, both running on the Torq-Coral NPU.
See the
[Readme](https://github.com/synaptics-astra-demos/sl2610-examples/blob/main/gemma_translate/README.md)
for more details.
## Image recognition
### A single CNN that detects, recognizes, and tracks objects in images.
Use a Yolov8 object detection model running on the NPU that can detect,
recognize, or track objects in the image frame. This demo is trained on the Coco
Common Objects dataset, and can be retrained on other datasets for specific use
cases, for example pose detection. A pose detection demo is also available.
See the
[Readme](https://github.com/synaptics-astra-demos/sl2610-examples/blob/main/object_detection/README.md)
for more details.
## Scene transcription
### Combines a Vision Transformer (ViT) and small LLM to describe the contents of an image using natural language.
Use a SigLip2 VisionTransformer running on the NPU to generate image tokens.
These tokens are fed into a quantized version of Gemma3-270M running on the NPU
which uses natural language to transcribe the image details.
See the
[Readme](https://github.com/synaptics-astra-demos/sl2610-examples/blob/main/scene_description/README.md)
for more details.
## Generative audio, aka "Jellectronica"
### Combines an image recognition CNN and audio generation RNN for a mixed modality pipeline that creates music.
Use a Yolo v8 object detection model running on the NPU that tracks
live-streamed or recorded jellyfish movements. These positional coordinates feed
into a MelodyRNN running on the CPU to create live music. A lightweight version
of the Google I/O 2026 preshow experience Jellectronica, running completely
on-device. Swap the video stream with your own and experiment with generative
concepts.
See the
[Readme](https://github.com/synaptics-astra-demos/sl2610-examples/blob/main/jellectronica/README.md)
for more technical details.
### Jellectronica at Google I/O 2026
[Video](https://www.youtube.com/watch?v=nG1bDAcJY9w)
This Jellectronica demo is being featured at the developers' conference in May
of 2026, using a live webcam feed of jellyfish from the nearby Monterey Bay
Aquarium. This stunning video serves as the real-time data source for a novel
demonstration of several new Google AI technologies.
The creative intent behind this project is to demonstrate that AI, particularly
in music, can enable new forms of creativity rather than replacing human effort.
It also demonstrates the potential of hyper-efficient edge compute to enable
powerful AI experiences. The Jellectronica application creates a
"human-jellyfish AI collaboration" to achieve something not previously possible.
#### How it works
Jellectronica transforms the motion of the jellyfish into an evolving symphony
by combining real-time edge processing with cutting-edge generative AI models:
- **Real-time object detection** : The system processes a 720p live video feed from the aquarium using Python and a custom-trained [YOLOv8](https://yolov8.com/) (You Only Look Once) object detection model. This tracks the swimming jellyfish in real-time at 20 frames-per-second.
- **High-fidelity audio with Lyria 3** : The positional data gathered from the jellyfish is used to generate MIDI, which triggers music made with Google's Lyria 3 model. Created by Google DeepMind, [Lyria
3](https://deepmind.google/models/lyria/) is Google's most advanced AI music generation model, allowing users to build high-fidelity music tracks directly from simple text descriptions or images.
- **Edge ML acceleration**: To process this feed efficiently, the ML model code runs on the Coral NPU accelerator. This accelerator is part of the Synaptics Astra™ SL2619 SoC, housed on a Coralboard development platform.
#### Motion to music
How do swimming jellyfish conduct a live soundtrack?
The Coral NPU processor ingests the live video feed and converts the motion of
jellyfish into a stream of positional data. This positional data is output as
raw numbers to higher-level machines where the numbers are converted to MIDI
musical data. The system uses pre-generated digital musical stems created by
Lyria. Stems are individual, grouped audio tracks (such as drums, vocals, or
bass) exported from a mixed song to allow for independent remixing and
manipulation.
The positional and interaction data of the jellyfish are used to trigger and
select these stems, controlling factors such as the intensity and type of
instrumentation. Because the jellyfish dictate these factors, every moment of
the Jellectronica experience is uniquely orchestrated by nature in real-time.
## And more ...
Look for more good stuff in the [Synaptics Developer
Zone](https://developer.synaptics.com).
*** ** * ** ***