Abstract: Described are techniques for software test reliability characterization. The techniques include receiving a test failure for a software under development in an Integrated Development Environment (IDE). The techniques further include identifying, from a software testing repository communicatively coupled to the IDE, a similar failed software test. The techniques further include applying a reliability index of the similar failed software test to the test failure. The techniques further include displaying, on a graphical user interface communicatively coupled to the IDE, a graphical illustration indicating the reliability index associated with the test failure.

Abstract: One embodiment of the present invention sets forth a technique for combining a color image and a monochrome image. The technique includes inputting a first set of pixel values from the color image and a second set of pixel values from the monochrome image into a neural network. The technique also includes applying, via a first set of neural network layers, one or more downsampling operations and one or more upsampling operations to feature maps associated with the inputted pixel values to generate a first output image that includes a first set of color values for a first set of pixel locations in the monochrome image. The technique further includes applying a second set of neural network layers to the first set of color values to generate a second output image that includes a second set of color values that is larger than the first set of color values.

Abstract: A system for recommending Buy Now, Pay Later (BNPL) offers receives a request for the recommended BNPL loan offers. The request is associated with a transaction. The system retrieves a BNPL loan offer similarity matrix and a transaction similarity matrix from a database. The system also retrieves consumer historical transaction records associated with the consumer from the database. Using the transaction data, the BNPL loan offer similarity matrix, and the transaction similarity matrix, the system performs both a content-based recommendation calculation and an experience-based recommendation calculation. The system then produces the recommended BNPL loan offers based on the results of the two calculations and transmits the recommended BNPL loan offers to a merchant for completing the transaction.

Abstract: In one embodiment, a method includes accessing a video captured by cameras which is associated with a first framerate lower than a threshold framerate, for any two adjacent frames of the accessed video: generating a warped frame from the two adjacent frames based on an optical flow associated with the two adjacent frames, determining alignments for the two adjacent frames, respectively, fusing the determined alignments for the two adjacent frames, and generating a reconstructed frame based on the fused alignment, and reconstructing the accessed video based on the any two adjacent frames and their respective reconstructed frames, wherein the reconstructed video is associated with a second framerate higher than the threshold framerate.

Abstract: Described are techniques for software test reliability characterization. The techniques include receiving a test failure for a software under development in an Integrated Development Environment (IDE). The techniques further include identifying, from a software testing repository communicatively coupled to the IDE, a similar failed software test. The techniques further include applying a reliability index of the similar failed software test to the test failure. The techniques further include displaying, on a graphical user interface communicatively coupled to the IDE, a graphical illustration indicating the reliability index associated with the test failure.

Abstract: An example operation may include one or more of tracking debugging actions performed to a software system via a runtime environment of the debugging actions, identifying one or more debugging attributes of an object of the software system based on the tracked debugging actions performed to the software system, generating a window which includes details of the one or more identified debugging attributes of the object, and displaying the window which includes the details of the one or more identified debugging attributes via a user interface of a debugging program.

Abstract: In one embodiment, a method for machine learning acceleration includes receiving instructions to perform convolution on an input tensor using a filter tensor, determining that the size of a first dimension of the input tensor is less than a processing capacity of each of multiple subarrays of computation units in a tensor processor, selecting a second dimension of the input tensor along which to perform the convolution, selecting, based on the second dimension, one or more dimensions of the filter tensor, generating (1) first instructions for reading, using vector read operations, activation elements in the input tensor organized such that elements with different values in the second dimension are stored contiguously in memory, and (2) second instructions for reading weights of the filter tensor along the selected one or more dimensions, and using the first and second instructions to provide the activation elements and the weights to the subarrays.

Abstract: In one embodiment, a method for machine learning acceleration includes receiving, by a shared controller of a tensor processor cluster that includes multiple tensor processors, a multi-cycle instruction, determining, based on the instruction, a sequence of vector operations to be executed by the tensor processors and address information usable to determine a respective spatial partition of an input tensor on which each tensor processor is to operate when performing each vector operation. The method also includes, for each vector operation in the sequence, generating, based on the address information, a common address offset, relative to a respective base address associated with each tensor processor, at which each tensor processor is to retrieve the respective spatial partition on which the tensor processor is to operate, multicasting the common address offset to the tensor processors, and controlling the tensor processors to execute the vector operation in parallel and in lock step.

Abstract: In one embodiment, a method includes accessing a video captured by cameras which is associated with a first framerate lower than a threshold framerate, for any two adjacent frames of the accessed video: generating a warped frame from the two adjacent frames based on an optical flow associated with the two adjacent frames, determining alignments for the two adjacent frames, respectively, fusing the determined alignments for the two adjacent frames, and generating a reconstructed frame based on the fused alignment, and reconstructing the accessed video based on the any two adjacent frames and their respective reconstructed frames, wherein the reconstructed video is associated with a second framerate higher than the threshold framerate.

Abstract: Disclosed herein is a method for using a neural network across multiple devices. The method can include receiving, by a first device configured with a first one or more layers of a neural network, input data for processing via the neural network implemented across the first device and a second device. The method can include outputting, by the first one or more layers of the neural network implemented on the first device, a data set that is reduced in size relative to the input data while identifying one or more features of the input data for processing by a second one or more layers of the neural network. The method can include communicating, by the first device, the data set to the second device for processing via the second one or more layers of the neural network implemented on the second device.

Abstract: A method of using computer-generated predicted image frames to create a high-dynamic-range (HDR) video is described. A method includes receiving first and second captured image frames via an image sensor. The first captured image frame represents a scene in the real-world at a first point in time and the second captured image frame represents the scene in the real-world at a second point in time that is after the first point in time. The method further includes in accordance with a determination that the first captured image frame and the second captured image frame will be used to produce an HDR video, generating a computer-generated predicted image frame representing the scene in the real-world at a time between the first point in time and the second point in time and fusing the second image frame with the computer-generated predicted image frame to generate an HDR frame for the HDR video.

Abstract: A method for providing performance assessment of terminal devices is provided. A user initiates, by way of a service application that runs on a user device of the user, a first request for obtaining risk scores or connectivity scores of the terminal devices. The first request may include terminal identifiers of specific terminal devices or information pertaining to a specific geographical area. The user device communicates the first request to a server. The server determines the risk scores or the connectivity scores based on the first request. The server transmits, to the user device, a first response that includes the risk scores or the connectivity scores. The user device displays the risk scores or the connectivity scores to the user based on the first response, thereby providing the performance assessment of the terminal devices.

Abstract: According to one embodiment of the present disclosure, a device comprises a latching circuitry, where the latching circuitry comprises at least one correlated electron random access memory (CeRAM) element. The latching circuitry further comprises a control circuit coupled to the at least one CeRAM element. The control circuit is configured to receive at least one control signal. Based on the at least one control signal, perform at least one of storing data into the latching circuitry and outputting data from the latching circuitry.

Abstract: In one embodiment, a method for machine learning acceleration includes receiving, by a shared controller of a tensor processor cluster that includes multiple tensor processors, a multi-cycle instruction, determining, based on the instruction, a sequence of vector operations to be executed by the tensor processors and address information usable to determine a respective spatial partition of an input tensor on which each tensor processor is to operate when performing each vector operation. The method also includes, for each vector operation in the sequence, generating, based on the address information, a common address offset, relative to a respective base address associated with each tensor processor, at which each tensor processor is to retrieve the respective spatial partition on which the tensor processor is to operate, multicasting the common address offset to the tensor processors, and controlling the tensor processors to execute the vector operation in parallel and in lock step.

Abstract: An approach is provided for predicting an exception during a debugging of software code before the debugging encounters the exception. During a debugging of a line number L of the code and based on an exception being predicted to be encountered at a line number M, a warning is displayed that the exception is to be encountered at the line number M, which is within a range of line numbers L+1 through L+X, where L>0 and X>1. Using a fix written in response to the predicted exception, the software code is modified. During a debugging of the line number M of the code, the modified software code is executed to avoid the predicted exception.

Abstract: Disclosed herein includes a system, a method, and a device including shift circuitry and add circuitry for performing multiplication of a first value and a second value for a neural network. The first value has a predetermined format including a first bit, and two or more second bits to represent a value of zero or 2n where n is an integer greater than or equal to 0. The device shifts, when the two or more second bits represent the value of 2n, the second value by (n+1) bits via the shift circuitry to provide a first result, selectively outputs zero or the second value, based on a value of the first bit of the first value, to provide a second result, and adds the first result and the second results via the add circuitry to provide a result of the multiplication of the first and second values.

Abstract: Broadly speaking, embodiments of the present technique provide a neuron for a spiking neural network, where the neuron is formed of at least one Correlated Electron Random Access Memory (CeRAM) element or Correlated Electron Switch (CES) element.

Abstract: Disclosed herein includes a system, a method, and a device including shift circuitry and add circuitry for performing multiplication of a first value and a second value for a neural network. The first value has a predetermined format including a first bit, and two or more second bits to represent a value of zero or 2n where n is an integer greater than or equal to 0. The device shifts, when the two or more second bits represent the value of 2n, the second value by (n+1) bits via the shift circuitry to provide a first result, selectively outputs zero or the second value, based on a value of the first bit of the first value, to provide a second result, and adds the first result and the second results via the add circuitry to provide a result of the multiplication of the first and second values.

Abstract: Disclosed herein is a method for using a neural network across multiple devices. The method can include receiving, by a first device configured with a first one or more layers of a neural network, input data for processing via the neural network implemented across the first device and a second device. The method can include outputting, by the first one or more layers of the neural network implemented on the first device, a data set that is reduced in size relative to the input data while identifying one or more features of the input data for processing by a second one or more layers of the neural network. The method can include communicating, by the first device, the data set to the second device for processing via the second one or more layers of the neural network implemented on the second device.

Abstract: A method for providing performance assessment of terminal devices is provided. A user initiates, by way of a service application that runs on a user device of the user, a first request for obtaining risk scores or connectivity scores of the terminal devices. The first request may include terminal identifiers of specific terminal devices or information pertaining to a specific geographical area. The user device communicates the first request to a server. The server determines the risk scores or the connectivity scores based on the first request. The server transmits, to the user device, a first response that includes the risk scores or the connectivity scores. The user device displays the risk scores or the connectivity scores to the user based on the first response, thereby providing the performance assessment of the terminal devices.