Abstract: Disclosed are systems, methods and computer-readable media for controlling and managing the identification and provisioning of resources within an on-demand center as well as the transfer of workload to the provisioned resources. One aspect involves creating a virtual private cluster within the on-demand center for the particular workload from a local environment. A method of managing resources between a local compute environment and an on-demand environment includes detecting an event associated with a local compute environment and based on the detected event, identifying information about the local environment, establishing communication with an on-demand compute environment and transmitting the information about the local environment to the on-demand compute environment, provisioning resources within the on-demand compute environment to substantially duplicate the local environment and transferring workload from the local-environment to the on-demand compute environment.

Abstract: A cell carrier for a battery module, a battery module, and a method for assembling a battery module. The battery module includes a plurality of pairs of battery cells and the cell carrier. The cell carrier includes a plurality of recesses, each including a first recess portion including a first sidewall portion and a first bottom portion, and a second recess portion including a second sidewall portion and a second bottom portion. The first bottom portion and the second bottom portion of each recess include a shared cutout. For each pair of battery cells, a first end of a first battery cell is coupled to the first recess portion and a first end of a second battery cell is coupled to the second recess portion of a respective recess.

Abstract: A convolution calculation engine includes a kernel element counter for a convolution operation between a kernel and an input tensor. The kernel element counter wraps back to an initial kernel count value after reaching a maximum kernel count value. The convolution calculation engine also includes an offset look-up table (LUT) that provides a relative input offset into the input tensor based on an output of the kernel element counter and input location calculation logic that provides an input location within an input tensor for the convolution operation based on the relative input offset provided by the offset LUT.

Abstract: A convolution calculation engine to perform a convolution operation includes a convolution address compute unit. The convolution address compute unit includes an outer output base location register to provide an outer output base location for the convolution operation and an outer input base location register to provide an outer input base location for the convolution operation. It also includes a kernel element counter that starts to count from an initial kernel count value to a maximum kernel count value in response to a change in the outer output base location and a kernel offset generator to generate a kernel offset based on an output of the kernel element counter. In addition, the convolution address compute unit includes inner location logic to calculate an output location based on the outer output base location and an input location based on the outer input base location and output of the kernel element counter.

Abstract: A system includes a surface device, preferably positioned on a surface, a downhole device, and a wireline communications system. A downlink communication between the surface device and the downhole device occurs via Hopped Frequency Shift Keying (HFSK) voltage-modulated signals. An optional uplink communication between the downhole device and the surface device may occur via Frequency Shift Keying (FSK) current-modulated signals. The downhole device may comprise an addressable switch.

Abstract: A mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.

Abstract: Introduced here is a computational architecture (also referred to as a “computational infrastructure”) that addresses the limitations of traditional data management solutions using a highly secure data management solution coupled with consent-based sharing. At a high level, the computational architecture applies blockchain methodologies to both transaction data and business data such that both types of data are stored “on chain” in the same computational architecture. This enables several significant advantages over traditional data management solutions with respect to data security, data ownership, data sharing, and intelligence.

Abstract: An on-demand compute environment comprises a plurality of nodes within an on-demand compute environment available for provisioning and a slave management module operating on a dedicated node within the on-demand compute environment, wherein upon instructions from a master management module at a local compute environment, the slave management module modifies at least one node of the plurality of nodes.

Abstract: A control system for a drive through includes processing circuitry. The processing circuitry is configured to determine, using image data obtained from multiple cameras, a characteristic of a vehicle in a lane of the drive through. The processing circuitry can operate a point of sale unit to provide an indication of the characteristic of the vehicle. The processing circuitry can, responsive to detection of one or more of a plurality of vehicles using the image data, perform at least one of (i) initiating an alert via the point of sale unit for staff of the drive through, (ii) initiating an action to open an additional lane, or (iii) initiating a kitchen action to prepare one or more food or beverage items.

Abstract: A computing method comprises combining an M×K multiplicand matrix and P number of addend vectors to generate an M×(K+P) integrated matrix. The addend vectors can comprise a vector of constants and/or a column of an addend matrix. The method further comprises generating a row-extended matrix comprising a K×N multiplicand matrix and P rows of a constant vector. The method computes (K+P) products of a row of the integrated matrix multiplied by a column of the row-extended matrix and computing an integrated sum of the products. A multiply-accumulate computation can compute the integrated sum and is equivalent to a sum of K number of products of a column of the M×K matrix multiplied by a row of the K×N multiplicand matrix and added to the P number of addend vectors. A computing system can implement the method and can include a matrix computation unit.

Abstract: A traffic management system for controlling traffic flow in an area is provided. The system has sensors (10) positioned at respective junctions (2) in the area, forming a network of nodes (2). Software (25) processes the sensor signals to derive vehicle signatures indicative of a particular vehicle detected at the node at a particular time. This is used to track the progress of vehicles traveling across the network and derive vehicle statistics (60) as to traffic volumes (64), routes (66) and journey times (62) at various times. Artificial intelligence (AI) (80) trained on historical vehicle statistics arranged to predict traffic arriving at plural junctions at a future time based on receiving a current count of vehicles sensed at nodes in the network. Based on the prediction, a control plan for traffic lights (3) is determined to optimise traffic flow.

Abstract: A computer system includes an array of reconfigurable processor blocks which execute fragments of a larger data processing operation. An array controller distributes a control signal to the reconfigurable processors in the array and receives control signals for the respective execution fragments. The control signal may include quiesce logic or other control methods to execute the effective execution fragments of the larger data processing operation when individual processors become available.

Abstract: A compiler generates a configuration file to configure a fracturable data path in a coarse-grained reconfigurable processor. The configuration file, when loaded into the reconfigurable processor enables a fracturable data path in a configurable unit of the reconfigurable processor to produce multiple independent address sequences by analyzing two address calculations to determine the number of pipeline stages for each calculation. The configuration file includes first and second configuration data for distinct sets of computational stages within the pipelined computation stages, allowing the processor to generate a first address sequence using N pipeline stages and a second address sequence using M pipeline stages, where N and M are positive integers.

Abstract: A logic unit in an array of processing units is configurable to consume source tokens and a status signal and to produce barrier tokens and an enable signal based on the source tokens and the status signal.

Abstract: A consent block is a type of block that may be stored in a blockchain. Each consent block has an owner and may store an owner consent contract, i.e., a smart contract containing owner-specified access rules that determine who may access data assets that are stored in other blocks of the blockchain and owned by the same owner. The consent block may alternatively store a global consent contract containing global access rules that supersede owner-specified access rules. The consent block also stores a hash value determined from the consent contract and a previous hash value of the block immediately preceding the consent block. The consent contract and the position of the consent block in the blockchain are verifiable from the hash value. Each consent block, once added to the blockchain, becomes part of the immutable record of data stored in the blockchain, and therefore leaves an auditable trail.

Abstract: A complier produces a configuration file to configure a fracturable data path of a configurable unit in a coarse-grained reconfigurable processor to concurrently generate different address sequences generated using different address associated with different operations. The fracturable data path includes multiple computation stages respectively including a pipeline register. The compiler analyzes a first address calculation and a second address calculation and assigns a first set of stages to the first operation to generate the first address sequence and a second set of stages to the second operation to generate the second address sequence using the second set of stages, based on the analysis. A configuration file for the configurable unit is generated by the compiler that assigns the first set of stages to the first operation and the second set of stages to the second operation and includes two or more immediate values for each computation stage.

Abstract: A reconfigurable data processor comprises an array of configurable units configurable to allocate a plurality of sets of configurable units in the array to implement respective execution fragments of the data processing operation. Quiesce logic is coupled to configurable units in the array, configurable to respond to a quiesce control signal to quiesce the sets of configurable units in the array on quiesce boundaries of the respective execution fragments, and to forward quiesce ready signals for the respective execution fragments when the corresponding sets of processing units are ready. An array quiesce controller distributes the quiesce control signal to configurable units in the array, and receives quiesce ready signals for the respective execution fragments from the quiesce logic.

Abstract: Introduced here is a computational architecture (also referred to as a “computational infrastructure”) that addresses the limitations of traditional data management solutions using a highly secure data management solution coupled with consent-based sharing. At a high level, the computational architecture applies blockchain methodologies to both transaction data and business data such that both types of data are stored “on chain” in the same computational architecture. This enables several significant advantages over traditional data management solutions with respect to data security, data ownership, data sharing, and intelligence.

Abstract: Disclosed are systems, methods and computer-readable media for controlling and managing the identification and provisioning of resources within an on-demand center as well as the transfer of workload to the provisioned resources. One aspect involves creating a virtual private cluster within the on-demand center for the particular workload from a local environment. A method of managing resources between a local compute environment and an on-demand environment includes detecting an event associated with a local compute environment and based on the detected event, identifying information about the local environment, establishing communication with an on-demand compute environment and transmitting the information about the local environment to the on-demand compute environment, provisioning resources within the on-demand compute environment to substantially duplicate the local environment and transferring workload from the local-environment to the on-demand compute environment.

Abstract: Disclosed are systems, methods and computer-readable media for controlling and managing the identification and provisioning of resources within an on-demand center as well as the transfer of workload to the provisioned resources. One aspect involves creating a virtual private cluster within the on-demand center for the particular workload from a local environment. A method of managing resources between a local compute environment and an on-demand environment includes detecting an event associated with a local compute environment and based on the detected event, identifying information about the local environment, establishing communication with an on-demand compute environment and transmitting the information about the local environment to the on-demand compute environment, provisioning resources within the on-demand compute environment to substantially duplicate the local environment and transferring workload from the local-environment to the on-demand compute environment.