Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: A system and method for representing events that occur in a real world deployment is described. A real-world workload including multiple events is identified. Multiple characteristics of the real-world workload are converted into multiple endpoint simulator workloads. Multiple gateway hardware characteristics are converted into a modeling elements for simulated Internet of things (IoT) networks. Further, a simulation is performed for each of the endpoint simulator workloads on each of the simulated IoT networks. Also, statistics are collected about the performance of the simulated IoT networks for the endpoint simulator workloads.

Abstract: A computing device and method for profiling and diagnostics in an Internet of Things (IoT) system, including matching an observed solution characteristic of the IoT system to an anomaly in an anomaly database.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: A computing device and method for profiling and diagnostics in an Internet of Things (IoT) system, including matching an observed solution characteristic of the IoT system to an anomaly in an anomaly database.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: A system and method for representing events that occur in a real world deployment is described. A real-world workload including multiple events is identified. Multiple characteristics of the real-world workload are converted into multiple endpoint simulator workloads. Multiple gateway hardware characteristics are converted into a modeling elements for simulated Internet of things (IoT) networks. Further, a simulation is performed for each of the endpoint simulator workloads on each of the simulated IoT networks. Also, statistics are collected about the performance of the simulated IoT networks for the endpoint simulator workloads.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: A system and method for representing events that occur in a real world deployment is described. A real-world workload including multiple events is identified. Multiple characteristics of the real-world workload are converted into multiple endpoint simulator workloads. Multiple gateway hardware characteristics are converted into a modeling elements for simulated Internet of things (IoT) networks. Further, a simulation is performed for each of the endpoint simulator workloads on each of the simulated IoT networks. Also, statistics are collected about the performance of the simulated IoT networks for the endpoint simulator workloads.

Abstract: A computing device and method for profiling and diagnostics in an Internet of Things (IoT) system, including matching an observed solution characteristic of the IoT system to an anomaly in an anomaly database.

Abstract: A computing device and method for profiling and diagnostics in an Internet of Things (IoT) system, including matching an observed solution characteristic of the IoT system to an anomaly in an anomaly database.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: A computing device and method for profiling and diagnostics in an Internet of Things (IoT) system, including matching an observed solution characteristic of the IoT system to an anomaly in an anomaly database.

Abstract: The techniques disclosed herein include a computing device for Internet of Things (IoT) solution sizing. The computing device is to determine a solution deployment metric, trigger edge traffic, monitor a round trip characteristic and an actuation pattern, execute permutations of input workloads, and determine a solution deployment.

Abstract: A method and apparatus including a computing device to quantify a term of a service agreement in a context of a proposed solution, evaluate solution characteristics against a given gateway architecture, and compare the solution characteristics to desired service-agreement solution metrics. The solution characteristics include simulated observed characteristics.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: A computing device and method for profiling and diagnostics in an Internet of Things (IoT) system, including matching an observed solution characteristic of the IoT system to an anomaly in an anomaly database.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: A method and apparatus including a computing device to quantify a term of a service agreement in a context of a proposed solution, evaluate solution characteristics against a given gateway architecture, and compare the solution characteristics to desired service-agreement solution metrics. The solution characteristics include simulated observed characteristics.