The Torq Edge AI Platform enables execution of diverse neural networks, efficiently utilizing NPU acceleration. The Torq tools and compiler allow you to convert a model from its original format to an representation optimized for the targeted Torq NPU. In this tutorial, you will learn how to bring your own model, optimize it for the Synaptics Astra SL261x processors, and test model inference speeds.
A compact 270M parameter multimodal language model from Google optimized for text classification and data extraction tasks with rapid fine-tuning capability.
A quantized MobileNet V2 model with a 1.0 width multiplier optimized for image classification on ImageNet at 224x224 resolution.
A high-efficiency automatic speech recognition model using variable-length architecture for real-time speech recognition, optimized for 16kHz mono audio.
torq
A YOLOv8n model for object detection optimized for 320x320 resolution, offering a balance between speed and accuracy.
A YOLOv8s model for object detection optimized for 320x320 resolution, offering a balance between speed and accuracy.