Abstract: A system for optimizing a vision transformer block for use with mobile vision transformers utilized for tasks, such as image classification, segmentation, and objected detection is disclosed. The system includes incorporating a 1×1 convolutional layer in place of a 3×3 convolutional layer in a fusion block of the vision transformer block to reduce constraints on scaling neural network size. Additionally, the system includes fusing local and global representations in the fusion block of the vision transformer block instead of fusing input features and global representations. Furthermore, the system includes fusing input features in the fusion block by adding the input features to the output of the 1×1 convolutional layer of the fusion block. Moreover, the system includes substituting a 3×3 convolutional layer in the local representation block of the vision transformer block with a depthwise-separable 3×3 convolutional layer.

Abstract: A system for providing an enhanced vision transformer block for mobile vision transformers to perform computer vision tasks, such as image classification, segmentation, and objected detection is disclosed. A local representation block of the block applies a depthwise-separable convolutional layer to vectors of an input image to facilitate creation of local representation outputs associated with the image. The local representation output is fed into a global representation block, which unfolds the local representation outputs, applies vision transformers, and folds the result to generate a global representation output associated with the image. The global representation output is fed to a fusion block, which concatenates the local representations with the global representations, applies a point-wise convolution to the concatenation to generate a fusion block output, and fuses input features of the image with the fusion block out to generate an output to facilitate performance of a computer vision tasks.

Abstract: An artificial neural network is trained via reinforcement learning to receive first data representative of execution dependency conditions of instructions of a program, second data representative of a schedule of a first portion of the instructions of the program for execution in a device having a plurality of circuits units operable in parallel, and third data identifying a next instruction selected from a second portion of the instructions of the program remaining to be scheduled for execution in the device. The artificial neural network selects a placement of the next instruction in one of the circuit units from a plurality of possible placements of the next instruction in the device. Performance of placements of instructions being tested in search for a valid schedule for running the program in the device can be measured to generate samples to train the artificial neural network via reinforcement learning.

Abstract: A system, method and apparatus of image segmentation with semi supervised training of an artificial neural network using coarse labels. For example, a first artificial neural network is trained to perform image segmentation on first images according to fine labels of image segmentation for the first images and to perform image segmentation on second images according to coarse labels of image segmentation for the second images. After the training, the first artificial neural network is used to perform image segmentation of the second images to identify improved labels of image segmentation for the second images. Subsequently, a supervised machine learning technique can be used to train a second artificial neural network to perform image segmentation on the first images according to fine labels and on the second images according to the improved labels.

Abstract: A system, method and apparatus of memory failure prediction through image analyses using an artificial neural network. A sequence of images indicative of progress of memory failures in a region of an integrated circuit die can be generated according to a physical layout of memory cells in the region. The artificial neural network can be trained to recognize graphical features in early images in the sequence and to predict, based on the recognized graphical features, memory failures shown in subsequent images in the sequence. A computing apparatus can use the artificial neural network to analyze an input image shown current memory failures in the region and to identify one or more memory cells in the region that are likely to have subsequent memory failures.

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: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

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.