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As a machine learning technique, TinyML combines reduced and optimized machine learning applications that require hardware, system, and software components. These operate at the edge of the cloud in real time while converting data with minimal energy and cost.
Microcontrollers (MCUs) today can run deep learning computer vision algorithms at the edge—something that felt far out of reach just a few years ago. In this article, I discuss how to get started with your own artificial intelligence (AI) app prototype, using Seeed Studio's SenseCAP K1100 Kit.
Tomeu Vizoso has been leading the effort for supporting Vivante's NPU IP within the Etnaviv driver that began as a reverse-engineered driver for Vivante graphics
This PyTorch vs TensorFlow provides more insight into the machine learning frameworks created by Google and
The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. ...