Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for sparse tensor storage for neural network accelerators. An example apparatus includes sparsity map generating circuitry to generate a sparsity map corresponding to a tensor, the sparsity map to indicate whether a data point of the tensor is zero, static storage controlling circuitry to divide the tensor into one or more storage elements, and a compressor to perform a first compression of the one or more storage elements to generate one or more compressed storage elements, the first compression to remove zero points of the one or more storage elements based on the sparsity map and perform a second compression of the one or more compressed storage elements, the second compression to store the one or more compressed storage elements contiguously in memory.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to debug a hardware accelerator such as a neural network accelerator for executing Artificial Intelligence computational workloads. An example apparatus includes a core with a core input and a core output to execute executable code based on a machine-learning model to generate a data output based on a data input, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on at least one of the machine-learning model or the breakpoint. In response to the triggering of the breakpoint, the debug circuitry is to stop the execution of the executable code and output data such as the data input, data output and the breakpoint for debugging the hardware accelerator.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for sparse tensor storage for neural network accelerators. An example apparatus includes sparsity map generating circuitry to generate a sparsity map corresponding to a tensor, the sparsity map to indicate whether a data point of the tensor is zero, static storage controlling circuitry to divide the tensor into one or more storage elements, and a compressor to perform a first compression of the one or more storage elements to generate one or more compressed storage elements, the first compression to remove zero points of the one or more storage elements based on the sparsity map and perform a second compression of the one or more compressed storage elements, the second compression to store the one or more compressed storage elements contiguously in memory.

Abstract: Methods, apparatus, and processor-readable storage media for request processing for configurable information technology solutions are provided herein. An example method includes obtaining a request that is to be processed based on a current configuration of a system, wherein the system comprises a plurality of items and each item comprises a set of components, each of the components having one of a plurality of types; obtaining component-level information for the plurality of items from multiple data sources; determining the current configuration of the system by applying one or more rules to the component-level information that identify at least one particular one of the types of at least one of the components in each of one or more of the sets and a quantity of the components of the at least one particular type that are in the corresponding set; and processing the request based on the determined current configuration.

Abstract: Embodiments for assessing energy in a thermal energy fluid transfer system in a cloud computing environment by a processor. Behavior of the thermal energy fluid transfer system, associated with a heating service, a cooling service, or a combination thereof, may be learned according to collected data to identify one or more energy usage events. An energy usage assessment operation may be performed using temperature signal disambiguation operations, with data collected over a selected time period by one or more non-intrusive Internet of Things (IoT) sensors located at one or more selected positions in the thermal energy fluid transfer system, to learn the system performance indicators, and when coupled with ingested expected policy behavior, identify one or more energy usage waste events according to the learned behavior in real time.

Abstract: Methods, apparatus, and processor-readable storage media for request processing for configurable information technology solutions are provided herein. An example method includes obtaining a request that is to be processed based on a current configuration of a system, wherein the system comprises a plurality of items and each item comprises a set of components, each of the components having one of a plurality of types; obtaining component-level information for the plurality of items from multiple data sources; determining the current configuration of the system by applying one or more rules to the component-level information that identify at least one particular one of the types of at least one of the components in each of one or more of the sets and a quantity of the components of the at least one particular type that are in the corresponding set; and processing the request based on the determined current configuration.

Abstract: Methods, apparatus, and processor-readable storage media for request processing using dynamic item data structures are provided herein. An example method includes an item data structure comprising an item identifier for a given item and a plurality of global identifiers, wherein a given one of the global identifiers is indicative of a corresponding one of a plurality of different variations of the given item based on a particular combination of values associated with the given global identifier, wherein the particular combination of values is specified for a set of parameters associated with the given item; dynamically identifying one of the global identifiers in the item data structure based at least in part on one or more user inputs indicative of a value for at least one of the parameters; and processing a request for the given item based at least in part on the identified global identifier.

Abstract: Methods, apparatus, and processor-readable storage media for request processing using dynamic item data structures are provided herein. An example method includes an item data structure comprising an item identifier for a given item and a plurality of global identifiers, wherein a given one of the global identifiers is indicative of a corresponding one of a plurality of different variations of the given item based on a particular combination of values associated with the given global identifier, wherein the particular combination of values is specified for a set of parameters associated with the given item; dynamically identifying one of the global identifiers in the item data structure based at least in part on one or more user inputs indicative of a value for at least one of the parameters; and processing a request for the given item based at least in part on the identified global identifier.

Abstract: Embodiments for fault diagnosis and analysis of refrigeration condenser systems by a processor. An energy usage anomaly is detected in a condenser by comparing an energy usage profile of the condenser against a knowledge domain of energy usage standards and energy usage standards anomalies.

Abstract: Embodiments for implementing intelligent refrigeration compressor runtime schedule extraction in an Internet of Things (IoT) computing environment by a processor. Compressor runtime positions of a compressor may be determined according to a single phase signal taken from an energy meter. Performance and health status of the compressor may be established according to the one or more compressor runtime activities determined from the established nominal baseload classifier.

Abstract: Energy usage can be monitored within at least one building having a plurality of energy consuming components. A database can be generated that contains values for a set of data points corresponding to data received from the plurality of energy consuming components. A change in a configuration can be detected for the plurality of energy consuming components based upon a change in values received from plurality of energy consuming components relative to the database. Based upon the change, an additional data point can be added to the set of data points in the database. Based upon the values for the set of data points, a probability can be determined that a rule for the additional data point is valid. A message can then be generated that includes the determined probability.

Abstract: Embodiments for assessing energy usage efficiency in a fluid transfer pumping system in a cloud computing environment by a processor. A rate of temperature decay may be determined over a selected time period using a temperature signal collected by one or more non-intrusive Internet of Things (IoT) sensors located at one or more selected positions of a piping network in the fluid transfer pumping system so as to determine energy efficiency in the fluid transfer pumping system associated with a heating service, a cooling service, or combination thereof.

Abstract: Embodiments for implementing intelligent refrigeration compressor runtime schedule extraction in an Internet of Things (IoT) computing environment by a processor. Compressor runtime positions of a compressor may be determined according to a single phase signal taken from an energy meter. Performance and health status of the compressor may be established according to the one or more compressor runtime activities determined from the established nominal baseload classifier.

Abstract: Embodiments for implementing intelligent refrigeration state classification in an Internet of Things (IoT) computing environment by a processor. A signal from a single IoT sensor associated with a refrigeration system may be used to assist in automatically classifying refrigeration states according to a training phase and an operational phase.

Abstract: Embodiments for assessing energy in a thermal energy fluid transfer system in a cloud computing environment by a processor. Behavior of the thermal energy fluid transfer system, associated with a heating service, a cooling service, or a combination thereof, may be learned according to collected data to identify one or more energy usage events. An energy usage assessment operation may be performed using temperature signal disambiguation operations, with data collected over a selected time period by one or more non-intrusive Internet of Things (IoT) sensors located at one or more selected positions in the thermal energy fluid transfer system, to learn the system performance indicators, and when coupled with ingested expected policy behavior, identify one or more energy usage waste events according to the learned behavior in real time.

Abstract: Embodiments for fault diagnosis and analysis of refrigeration condenser systems by a processor. An energy usage anomaly is detected in a condenser by comparing an energy usage profile of the condenser against a knowledge domain of energy usage standards and energy usage standards anomalies.

Abstract: Embodiments for assessing energy usage efficiency in a fluid transfer pumping system in a cloud computing environment by a processor. A rate of temperature decay may be determined over a selected time period using a temperature signal collected by one or more non-intrusive Internet of Things (IoT) sensors located at one or more selected positions of a piping network in the fluid transfer pumping system so as to determine energy efficiency in the fluid transfer pumping system associated with a heating service, a cooling service, or combination thereof.

Abstract: Embodiments for assessing energy in a fluid transfer pump system in a cloud computing environment by a processor. A fluid flow rate may be cognitively determined according to a tracer stimulus, injected into the fluid transfer pump system, and adequately detected by one or more Internet of Things (IoT) sensors located at one or more selected positions of a piping network in the fluid transfer pump system.

Abstract: Energy usage can be monitored within at least one building having a plurality of energy consuming components. A database can be generated that contains values for a set of data points corresponding to data received from the plurality of energy consuming components. A change in a configuration can be detected for the plurality of energy consuming components based upon a change in values received from plurality of energy consuming components relative to the database. Based upon the change, an additional data point can be added to the set of data points in the database. Based upon the values for the set of data points, a probability can be determined that a rule for the additional data point is valid. A message can then be generated that includes the determined probability.

Abstract: Energy usage can be monitored within at least one building having a plurality of energy consuming components. A database can be generated that contains values for a set of data points corresponding to data received from the plurality of energy consuming components. A change in a configuration can be detected for the plurality of energy consuming components based upon a change in values received from plurality of energy consuming components relative to the database. Based upon the change, an additional data point can be added to the set of data points in the database. Based upon the values for the set of data points, a probability can be determined that a rule for the additional data point is valid. A message can then be generated that includes the determined probability.