Abstract: Certain aspects of the present disclosure provide techniques and apparatus for quantized machine learning. A quantized input matrix is accessed at a layer of a neural network, and a first interim value is generated in an accumulator by performing matrix multiplication, using the accumulator, of the quantized input matrix and a quantized weight matrix associated with the layer of the neural network. The first interim value is normalized based at least in part on one or more leading sign bits of the first interim value, and the normalized first interim value is dequantized. A second interim value is generated by applying a rounded right-shift operation to the dequantized normalized first interim value, and activation data is generated by applying an activation function to the second interim value.

Abstract: A device includes a vector register file, a memory, and a processor. The vector register file includes a plurality of vector registers. The memory is configured to store a permutation instruction. The processor is configured to access a periodicity parameter of the permutation instruction. The periodicity parameter indicates a count of a plurality of data sources that contain source data for the permutation instruction. The processor is also configured to execute the permutation instruction to, for each particular element of multiple elements of a first permutation result register of the plurality of vector registers, select a data source of the plurality of data sources based at least in part on the count of the plurality of data sources and populate the particular element based on a value in a corresponding element of the selected data source.

Abstract: The present invention extends to methods, systems, and computer program products for deriving unified insights ad logs from DevOps Cl/CD tools and pipeline data. In general, a data transformer facilitates data normalization and serialization converting raw data across multiple DevOps tools and stores the data into a Data Lake in accordance with a customized schema. A continuous orchestrator sequences, aggregates and contextualizes the logs, providing an intuitive way of troubleshooting issues across a DevOps environment, historical data for compliance and audit purposes, and a build manifest for root cause analysis. The continuous orchestrator also processes the logs and leverages a KPI framework, providing intelligent dashboards across 90+ KPI's and a plurality of different dimensions (Planning, Development/pipelines, security, quality, operations, productivity and source code) to help customers make smart decisions and do more with less.

Abstract: A device includes a processor that includes a rotation vector register file, a second vector register file, and multiply-accumulate circuitry (MAC). The rotation vector register file includes a rotation vector register. The rotation vector register file is configured to rotate data in the rotation vector register. The second vector register file includes a source vector register. The MAC is configured to receive first input data from the rotation vector register file and second input data from the source vector register.

Abstract: This application is directed to using a single instruction to initiate a sequence of computational operations related to a neural network. An electronic device receives a single instruction to apply a neural network operation to a set of M-bit elements stored in one or more input vector registers. In response to the single instruction, the electronic device implements the neural network operation on the set of M-bit elements to generate a set of P-bit elements by obtaining the set of M-bit elements from the one or more input vector registers, quantizing each of the set of M-bit elements from M bits to P bits, and packing the set of P-bit elements into an output vector register. P is smaller than M. In some embodiments, the neural network operation is a quantization operation including at least a multiplication with a quantization factor and an addition with a zero point.

Abstract: This application is directed to using a single instruction to initiate a sequence of computational operations related to a neural network activation function. An electronic device receives a single instruction to apply a linear activation operation to a set of first elements stored in one or more input vector registers. In response to the single instruction, the linear activation operation is implemented on the set of first elements to generate a set of output elements. For each first element, the electronic device detects a sign value of the respective first element, selects a respective scalar from one or more scalars based on the sign value, and applies the linear activation operation on the respective first element based on the selected respective scalar and a bias value to generate a respective element of the set of output elements. The electronic device quantizes the set of output elements.

Abstract: A device includes a vector register file, a memory, and a processor. The vector register file includes a plurality of vector registers. The memory is configured to store a permutation instruction. The processor is configured to access a periodicity parameter of the permutation instruction. The periodicity parameter indicates a count of a plurality of data sources that contain source data for the permutation instruction. The processor is also configured to execute the permutation instruction to, for each particular element of multiple elements of a first permutation result register of the plurality of vector registers, select a data source of the plurality of data sources based at least in part on the count of the plurality of data sources and populate the particular element based on a value in a corresponding element of the selected data source.

Abstract: The present invention extends to methods, systems, and computer program products for upgrading DevOps tools in DevOps toolchains. An upgraded version of a DevOps platform tool is detected. A snapshot of an existing tool image corresponding to a current version of the DevOps platform tool is taken. Cloud service provider profile information, existing tool profile information corresponding to the existing tool image, and replacement tool profile information corresponding to replacement tool image are accessed. A DevOps platform is upgraded including deploying a replacement tool image corresponding to the upgraded version in accordance with the cloud service provider profile information, the existing tool profile information, the replacement tool profile information, and a DevOps platform category. When the upgrade is complete, the existing tool image is removed and the DevOps platform pointed to the replacement tool image. The snapshot is retained in accordance with rollback settings.

Abstract: A user interface (UI) feature assists users in comparing items and making purchase decisions. One or more attributes for results of a search query are dynamically identified and ranked for presentation within the UI so that a user easily compares products based on features of interest. Additionally, other content of the UI, such as product images, rankings, etc. are dynamically ranked based on the search query and/or user context and presented in an order of relevance so that the user quickly identifies information for making meaningful shopping decisions.

Abstract: The present invention extends to methods, systems, and computer program products for automating DevOps toolchains. A user registers with a platform provider through a user interface that provides a unified self-service portal. Subsequently, the user can login to a platform configuration system to access a user landing page and/or dashboard. The user can select DevOps platform tools to be installed and enter configuration information. When the user is satisfied with selected DevOps platform tools and entered configuration, the user can select a “confirm” user-interface control (e.g., a button). In response, the selected DevOps tools are installed (with a relatively high level of automation) as DevOps platform. The user can also change DevOps platforms through the user interface (also with a relatively high level of automation), including migrating, deleting, and upgrading DevOps tools.

Abstract: The present invention extends to methods, systems, and computer program products for removing DevOps tools from DevOps toolchains. A user selection requesting DevOps tool deletion is received. A snapshot of a tool image corresponding to the DevOps tool is taken. Cloud service provider profile information and tool profile information corresponding to the tool image are accessed. The DevOps tool is deleted from a DevOps platform, including removing the tool image, in accordance with the cloud service provider profile information and the tool profile information. A tool registry is updated to indicate removal of the tool image from the DevOps platform. The snapshot is stored and retained in accordance with a time period specified in rollback settings.

Abstract: The present invention extends to methods, systems, and computer program products for replacing DevOps tools in DevOps toolchains. A user selection of a replacement DevOps platform tool associated with a DevOps platform category is received. Configuration information for the DevOps platform, including cloud service provider profile information and existing tool profile information is accessed. The existing tool is removed from the DevOps platform in accordance with the existing tool profile information, the DevOps platform category, and the service provider profile information. A replacement tool and replacement tool profile information are accessed. The replacement tool is deployed to the DevOps platform in accordance with the replacement tool profile information, the DevOps platform category, and the cloud service provider profile information.

Abstract: The present invention extends to methods, systems, and computer program products for deriving unified insights ad logs from DevOps CI/CD tools and pipeline data. In general, a data transformer facilitates data normalization and serialization converting raw data across multiple DevOps tools and stores the data into a Data Lake in accordance with a customized schema. A continuous orchestrator sequences, aggregates and contextualizes the logs, providing an intuitive way of troubleshooting issues across a DevOps environment, historical data for compliance and audit purposes, and a build manifest for root cause analysis. The continuous orchestrator also processes the logs and leverages a KPI framework, providing intelligent dashboards across 90+ KPI's and a plurality of different dimensions (Planning, Development/pipelines, security, quality, operations, productivity and source code) to help customers make smart decisions and do more with less.

Abstract: The present invention extends to methods, systems, and computer program products for deriving unified insights ad logs from DevOps Cl/CD tools and pipeline data. In general, a data transformer facilitates data normalization and serialization converting raw data across multiple DevOps tools and stores the data into a Data Lake in accordance with a customized schema. A continuous orchestrator sequences, aggregates and contextualizes the logs, providing an intuitive way of troubleshooting issues across a DevOps environment, historical data for compliance and audit purposes, and a build manifest for root cause analysis. The continuous orchestrator also processes the logs and leverages a KPI framework, providing intelligent dashboards across 90+ KPI's and a plurality of different dimensions (Planning, Development/pipelines, security, quality, operations, productivity and source code) to help customers make smart decisions and do more with less.

Abstract: The present invention extends to methods, systems, and computer program products for deriving unified insights ad logs from DevOps CI/CD tools and pipeline data. In general, a data transformer facilitates data normalization and serialization converting raw data across multiple DevOps tools and stores the data into a Data Lake in accordance with a customized schema. A continuous orchestrator sequences, aggregates and contextualizes the logs, providing an intuitive way of troubleshooting issues across a DevOps environment, historical data for compliance and audit purposes, and a build manifest for root cause analysis. The continuous orchestrator also processes the logs and leverages a KPI framework, providing intelligent dashboards across 90+ KPI's and a plurality of different dimensions (Planning, Development/pipelines, security, quality, operations, productivity and source code) to help customers make smart decisions and do more with less.

Abstract: The present invention extends to methods, systems, and computer program products for automating DevOps toolchains. A user registers with a platform provider through a user interface that provides a unified self-service portal. Subsequently, the user can login to a platform configuration system to access a user landing page and/or dashboard. The user can select DevOps platform tools to be installed and enter configuration information. When the user is satisfied with selected DevOps platform tools and entered configuration, the user can select a “confirm” user-interface control (e.g., a button). In response, the selected DevOps tools are installed (with a relatively high level of automation) as DevOps platform. The user can also change DevOps platforms through the user interface (also with a relatively high level of automation), including migrating, deleting, and upgrading DevOps tools.

Abstract: The present invention extends configuring development and operations pipelines across domains. A pipeline manager can form and manage pipelines that span any combination of domains and any combination of: public cloud resources, private cloud resources, user on-premise resources, etc., in accordance with appropriate (cloud andor on-premise) profile information. The pipeline manager can (re)configure a pipeline as appropriate to address alterations to workflows, upgrades to DevOps tools, removal of functionality from a workflow, etc. A pipeline framework enables customers to build no-code pipelines spanning domains for various use cases in a plug and play manner (Software engineering and SDLC pipelines, Salesforce Cl/CD pipelines, AI/ML, SaaS applications, Infrastructure as a code (IaC), etc). The pipeline framework enables users to integrate collaboration tools, notifications, and approval gates offering thresholds at each and every step.

Abstract: The present invention extends configuring development and operations pipelines. A pipeline manager can form and manage pipelines that span any combination of: public cloud resources, private cloud resources, user on-premise resources, etc., in accordance with appropriate (cloud and/or on-premise) profile information. The pipeline manager can (re)configure a pipeline as appropriate to address alterations to workflows, upgrades to DevOps tools, removal of functionality from a workflow, etc. A pipeline framework enables customers to build no-code pipelines for various use cases in a plug and play manner (Software engineering and SDLC pipelines, Salesforce CI/CD pipelines, AI/ML, SaaS applications, Infrastructure as a code (IaC), etc). The pipeline framework enables users to integrate collaboration tools, notifications, and approval gates offering thresholds at each and every step.

Abstract: The present invention extends configuring development and operations pipelines using access controlled gates and thresholds. A pipeline manager can form and manage pipelines that span any combination of: public cloud resources, private cloud resources, user on-premise resources, etc., in accordance with appropriate (cloud and/or on-premise) profile information. Pipelines can be configured with any of: quality gates, security gates, or thresholds. Role Based Access Control (RBAC) can be used to control access to and/or approval of gates and thresholds. A pipeline framework enables customers to build no-code pipelines for various use cases in a plug and play manner. The pipeline framework also enables users to integrate collaboration tools, notifications, and approval gates offering thresholds at each and every step. In addition, the pipeline framework captures logs and provides a summary via livestream and also upon completion of each pipeline activity and after each pipeline.

Abstract: The present invention extends configuring development and operations pipelines using drag and drop techniques. Through user interface gestures, users can form and manage pipelines that span any combination of: public cloud resources, private cloud resources, user on-premise resources, etc., in accordance with appropriate (cloud and/or on-premise) profile information. The user interface facilitates pipeline (re)configuration as appropriate to address alterations to workflows, upgrades to DevOps tools, removal of functionality from a workflow, etc. The user interface enables customers to build no-code pipelines for various use cases in a drag and drop manner. Users can integrate collaboration tools, notifications, and approval gates offering thresholds at each and every step. In addition, the pipeline framework captures logs and provides a summary via livestream and also upon completion of each pipeline activity and after each pipeline.