Abstract: A system, method and apparatus to label video images with assistance from an artificial neural network. After a user provides first inputs to label first aspects of an object shown in a first video frame, the artificial neural network infers or predicts second aspects to be labeled for the object in a second video frame. A graphical user interface presents the inferred or predicted second aspects over a display of the second video frame to allow the user to confirm or modify the inference or prediction. For example, an object of interest in the first frame can be labeled with a classification and a bounding box; and the artificial neural network is trained to infer or predict, for the corresponding object in the second frame, its bounding box, classification, and pixels represented of the image of the object in the second frame.

Abstract: Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, an integrated circuit device may be configured to execute instructions with matrix operands and configured with random access memory (RAM). A compiler has an artificial neural network configured to identify an optimized compilation option for an artificial neural network to be compiled by the compiler and/or for a hardware platform of Deep Learning Accelerators. The artificial neural network of the compiler can be trained via machine learning to identify the optimized compilation option based on the features of the artificial neural network to be compiled and/or features of the hardware platform on which the compiler output will be executed.