42 docs tagged with "SL1600"

Build a Q&A assistant that picks answers using on-device embeddings.

Let an on-device AI model control an LED and system functions via tool calling.

Measure the inference speed of pre-trained YOLO models on Astra.

Compile your own model with the SyNAP toolkit and run it on the SL1600 NPU.

Build llama-cpp-python on the board for local LLM inference from Python.

A preprocessing model to convert NV12 formatted images from 1920x1080 resolution to RGB format at the same resolution.

A preprocessing model to convert NV12 formatted images from 1920x1080 resolution to RGB format at 224x224 resolution.

A preprocessing model to convert NV12 formatted images from 1920x1080 resolution to RGB format at 640x360 resolution.

Cross-compile llama.cpp on a host machine for Astra Machina.

Learn how text embeddings work and generate them on-device with Synaptics Astra.

Set up your board and run your first edge AI examples.

Recognize and label images with a classifier on the SL1600 NPU.

A quantized Inception V4 model optimized for image classification on ImageNet at 299x299 resolution.

Run small large language models locally on Astra.

Run Llama 3.2 1B on Astra as a single llamafile executable.

Run TinyLlama natively on Astra Machina with llama.cpp.

A MobileNet V1 model with a 0.25 width multiplier optimized for image classification on ImageNet at 224x224 resolution.

A quantized MobileNet V1 model with a 0.25 width multiplier optimized for image classification on ImageNet at 224x224 resolution.

A MobileNet V2 model with a 1.0 width multiplier optimized for image classification on ImageNet at 224x224 resolution.

A quantized MobileNet V2 model with a 1.0 width multiplier optimized for image classification on ImageNet at 224x224 resolution.

A lightweight MobileNet model optimized for people detection on high-resolution images.

A MobileNet model optimized for object detection on the COCO dataset with full resolution at 224x224 and 80 classes.

Modify the SyNAP framework to build your own C++ inference apps.

Build C++ apps that run models on the NPU with the SyNAP framework.

Find and locate objects in images with YOLO on the SL1600 NPU.

A PoseNet model using MobileNet architecture with 75% width multiplier for efficient body pose estimation.

A quantized PoseNet model using MobileNet architecture with 75% width multiplier for efficient and optimized body pose estimation.

Fine-tune YOLO11 for PPE detection with Roboflow and deploy it to Astra.

Transcribe speech on-device with Whisper and Moonshine.

A fast super-resolution model converting YUV images from 1280x720 to 3840x2160 resolution.

A fast super-resolution model converting YUV images from 1920x1080 to 3840x2160 resolution.

A QDEO-based super-resolution model converting YUV images from 1280x720 to 3840x2160 resolution.

A QDEO-based super-resolution model converting YUV images from 1920x1080 to 3840x2160 resolution.

Turn written text into natural-sounding speech on-device with Piper.

Export an Ultralytics YOLOv8 pose model and compile it for the Astra NPU.

Run vision language models that understand images and text with llama.cpp.

Transcribe a WAV file on-device with whisper.cpp.

Transcribe live microphone audio in real time with whisper.cpp.

A YOLOv5m model for object detection optimized for 640x480 resolution, offering a balance between speed and accuracy.

A YOLOv5s model for object detection optimized for 640x480 resolution, suitable for real-time applications.

A YOLOv5s model specialized for face detection, optimized for 640x480 resolution using ONNX with mixed quantization.

A YOLOv8s model specialized for body pose estimation, optimized for real-time applications.