Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

Abstract: Various examples disclosed herein relate to neural network quantization techniques, and more particularly, to selecting inference precisions for the layers of the neural network. In an example embodiment, a method is provided herein that includes determining an accuracy improvement of a layer of a neural network implemented using a first bit precision relative to using a second bit precision and determining a latency degradation of the layer of the neural network implemented using the first bit precision relative to using the second bit precision. The method further includes selecting, based on the accuracy improvement and the latency degradation, the first bit precision or the second bit precision for use in implementing the layer of the neural network.

Abstract: Technology is disclosed herein to execute an inference model by a processor which includes a reshape layer. In an implementation, the reshape layer of the inference model receives an output produced by a previous layer of the inference model and inserts padding into the output, then supplies the padded output as an input to a next layer of the inference model. In an implementation, the inference model includes a stitching layer at the beginning of the inference model and an un-stitch layer at the end of the model. The stitching layer of the inference model stitches together multiple input images into an image batch and supplies the image batch as an input to a subsequent layer. The un-stitch layer receives output from a penultimate layer of the inference model and unstitches the output to produce multiple output images corresponding to the multiple input images.

Abstract: Disclosed herein are systems and methods for determining the scaling factors for a neural network that satisfy the activation functions employed by the nodes of the network. A processor identifies a saturation point of an activation function. Next, the processor determines a scaling factor for an output feature map based on the saturation point of the activation function. Then, the processor determines a scaling factor for an accumulator based on the scaling for the output feature map and further based on a shift value related to a quantization. Finally, the processor determines a scaling factor for a weight map based on the scaling factor for the accumulator.

Abstract: Techniques including receiving a first set of values for processing by a machine learning (ML) network, storing a first portion of the first set of values in an on-chip memory, processing the first portion of the first set of values in a first layer of the ML network to generate a second portion of a second set of values, overwriting the stored first portion with the generated second portion, processing the second portion in a second layer of the ML network to generate a third portion of a third set of values, storing the third portion, repeating the steps of storing the first portion, processing the first portion, overwriting the stored first portion, processing the second portion, and storing the third portion for a fourth portion of the first set of values until all portions of the first set of values are processed to generate the third set of values.

Abstract: A technique for bit depth up-conversion including obtaining an input value for a computation in a first bit depth with a fewer number of bits as compared to a second bit depth, converting the input value from the first bit depth to the second bit depth as an unsigned data value, adjusting a pointer to the converted input value based on the first bit depth, performing the computation based on the adjusted pointer to obtain an adjusted output value, and performing a right shift operation on the adjusted output value based on the first bit depth to obtain an output value.

Abstract: In described examples, an integrated circuit includes a memory storing weights and biases, an N-bit fixed point matrix operations accelerator, and a processor. Starting with a first convolution layer, a convolution layer modeled using the processor receives input feature values. A feature scale and weight scale are reduced if an accumulator scale is greater than a maximum bias scale. The input feature values are rescaled using the feature scale, the weights are quantized using the weight scale, and the biases are quantized using the feature scale and weight scale. The rescaled input feature values and quantized weights and biases are convolved using the N-bit fixed point matrix operations accelerator to generate output feature values. The process repeats from the receive action using the output feature values as the input feature values of the next convolution layer. The process then repeats for all layers, feeding back an output feature range.

Abstract: Techniques for enhancing machine learning (ML) model execution. The technique includes determining an amount of memory used to process layers of a machine learning network having multiple layers, smoothing the amount of memory used to process the layers of the machine learning network based on a number of layers, identifying change layers where the smoothed amount of memory used changes more than a memory change threshold amount, grouping the layers of the machine learning network into a first layer grouping based on the identified change layers, and outputting the first layer grouping.

Abstract: A computer-implemented method includes determining first passages (FP) that provide evidence for one or more first relations (OOMFR) between first entities in a knowledge graph. The method includes generating an evidence index (EI) that associates the FP with the OOMFR and the first entities, receiving a query subsequent to generating the EI, and identifying, using the EI, the FP responsive to receiving the query. The method includes determining presentation aspects of the FP based on similarity information determined for the FP, and determining that second passages of the FP are substantially similar to at least one other passage of the FP. The method includes pruning the FP to generate a pruned set of passages (PSOP) that includes one or more third passages of the second passages and that does not include any other passages of the second passages. The method includes outputting the PSOP according to the presentation aspects.

Abstract: Techniques including receiving a first set of values for processing by a machine learning (ML) network, storing a first portion of the first set of values in an on-chip memory, processing the first portion of the first set of values in a first layer of the ML network to generate a second portion of a second set of values, overwriting the stored first portion with the generated second portion, processing the second portion in a second layer of the ML network to generate a third portion of a third set of values, storing the third portion, repeating the steps of storing the first portion, processing the first portion, overwriting the stored first portion, processing the second portion, and storing the third portion for a fourth portion of the first set of values until all portions of the first set of values are processed to generate the third set of values.

Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

Abstract: A computer-implemented method includes determining first passages (FP) that provide evidence for one or more first relations (OOMFR) between first entities in a knowledge graph. The method includes generating an evidence index (EI) that associates the FP with the OOMFR and the first entities, receiving a query subsequent to generating the EI, and identifying, using the EI, the FP responsive to receiving the query. The method includes determining presentation aspects of the FP based on similarity information determined for the FP, and determining that second passages of the FP are substantially similar to at least one other passage of the FP. The method includes pruning the FP to generate a pruned set of passages (PSOP) that includes one or more third passages of the second passages and that does not include any other passages of the second passages. The method includes outputting the PSOP according to the presentation aspects.

Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

Abstract: A method includes a computer device receiving a set of images for at least one user; the computer device receiving unique visual clue inputs from the at least one user for each image of the set of images; the computer device receiving drawing gesture inputs from the at least one user for each image of the set of images; and the computer device using the visual clue and drawing gesture inputs to create passwords to provide a locked access point for at least one device.