Implementazione hardware

Generazione di codice C/C++, generazione di codice GPU, Raspberry Pi^®, NVIDIA^® Jetson^®

Implementare i workflow di Deep Learning e Machine Learning nell'hardware.

Esempi in primo piano

Classify human electrocardiogram (ECG) signals by deploying a trained directed acyclic graph (DAG) network.

Create and deploy a Simulink^® model for signal classification using wavelet-based features.

Perform acoustic-based fault detection on a Raspberry Pi using wavelet scattering and recurrent neural networks.

Generate and deploy a CUDA^® executable to classify electrocardiogram signals using wavelet-derived features.

Classify human electrocardiogram signals on a Raspberry Pi using scalograms and a deep convolutional neural network.

Use wavelets to classify waveforms on a NVIDIA Jetson^®.