Bring Deep-Learning Inference to Embedded Applications

Deep learning, probably the most advanced and challenging foundation of artificial intelligence (AI), is having a significant impact and influence on many applications, enabling products to behave intelligently like humans. Favored by the introduction of higher-performance computers and systems for parallel computing, deep learning has today become a reality, especially in the field of image recognition and classification, voice recognition, text analysis, and virtual assistants. In recent years, we have witnessed the development of numerous models and architectures of neural networks (the basic structure on which deep learning is built), which led to the definition of data sets, ready to be used in real applications. Compared to traditional machine learning, deep learning can provide superior accuracy, greater versatility and use of big data. Models used in deep learning are based on deep neural networks (DNNs), which in turn can use different architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The choice of which architecture to use depends on the specific application: CNNs are particularly suited to image classification, while RNNs are normally used for text or speech recognition.