A merge sort accelerator (MSA) includes a pre-processing stage configured to receive an input vector and generate a pre-processing output vector based on a pre-processing instruction and the input vector. The MSA also includes a merge sort network having multiple sorting stages configured to be selectively enabled. The merge sort network is configured to receive the pre-processing output vector and generate a sorted output vector based on a sorting instruction and the pre-processing output vector. The MSA includes an accumulator stage configured to receive the sorted output vector and update an accumulator vector based on the accumulator instruction and the sorted output vector. The MSA also includes a post-processing stage configured to receive the accumulator vector and generate a post-processing output vector based on a post-processing instruction and the accumulator vector.

The present disclosure relates to a neuron for an artificial neural network. The neuron comprises a dot product engine operative to: receive a set of weights; receive a set of data inputs based on a set of input data signals; and calculate the dot product of the set of data inputs and the set of weights to generate a dot product engine output. The neuron further comprises an activation function module arranged to apply an activation function to a signal indicative of the dot product engine output to generate a neuron output; and gain control circuitry. The gain control circuitry is operative to control: an input gain applied to the input data signals to generate the set of data inputs; and an output gain applied to the dot product engine output or by the activation function module. The output gain is selected to compensate for the applied input gain.

A method to reconcile multiple instances of a single computer resource identified by resource discovery operations includes: (1) accessing information describing one or more resources; (2) identifying, via the accessed information, at least one resource that has been detected or discovered by at least two of the discovery operations; and (3) merging attributes associated with the identified resource from each of the at least two discovery operations into a single, reconciled resource object. Illustrative resources include, but are not limited to, computer systems, components of computer systems, data storage systems, switches, routers, memory, software applications (e.g., accounting and database applications), operating systems and business services (e.g., order entry or change management and tracking services).

A method to reconcile multiple instances of a single computer resource identified by resource discovery operations includes: (1) accessing information describing one or more resources; (2) identifying, via the accessed information, at least one resource that has been detected or discovered by at least two of the discovery operations; and (3) merging attributes associated with the identified resource from each of the at least two discovery operations into a single, reconciled resource object. Illustrative resources include, but are not limited to, computer systems, components of computer systems, data storage systems, switches, routers, memory, software applications (e.g., accounting and database applications), operating systems and business services (e.g., order entry or change management and tracking services).

Various aspects of the subject technology relate to methods, systems, and machine-readable media for generating histograms according to a set-theoretic approach. The method includes receiving a command to generate at least one histogram from selected data, the selected data comprising a plurality of column groups. The method also includes identifying a superset from the plurality of column groups. The method also includes materializing, in a memory, a superset multi-column group for the superset. The method also includes identifying at least one subset of the superset from the plurality of column groups, the at least one subset comprising at least one column group. The method also includes processing the at least one subset of the superset to form a grouped resultset. The method also includes generating the at least one histogram for the grouped resultset of the at least one subset of the superset.

Various aspects of the subject technology relate to methods, systems, and machine-readable media for generating histograms according to a set-theoretic approach. The method includes receiving a command to generate at least one histogram from selected data, the selected data comprising a plurality of column groups. The method also includes identifying a superset from the plurality of column groups. The method also includes materializing, in a memory, a superset multi-column group for the superset. The method also includes identifying at least one subset of the superset from the plurality of column groups, the at least one subset comprising at least one column group. The method also includes processing the at least one subset of the superset to form a grouped resultset. The method also includes generating the at least one histogram for the grouped resultset of the at least one subset of the superset.

A method for storing an object on storage nodes includes encrypting an object to be stored with a key. One or more hash values are computed for the object. The encrypted object is stored on the storage nodes. Storage location data is provided for the stored object. A transaction is computed for a blockchain, wherein information is encoded in the transaction, the encoded information representing the storage location data, the computed o hash values and key data. The transaction is stored in the blockchain provided by one or more blockchain nodes hosting the blockchain. A number of confirmations is provided for the transaction. The number of confirmations is compared with a predefined threshold confirmation number, wherein the predefined threshold confirmation number is computed such that with a pregiven certainty the encoded information in the transaction stored in the blockchain cannot be modified.

Disclosed herein are techniques for accelerating convolution operations or other matrix multiplications in applications such as neural network. In one example, an apparatus comprises a first circuit, a second circuit, and a third circuit. The first circuit is configured to: receive first values in a first format, the first values being generated from one or more asymmetric quantization operations of second values in a second format, and generate difference values based on subtracting a third value from each of the first values, the third value representing a zero value in the first format. The second circuit is configured to generate a sum of products in the first format using the difference values. The third circuit is configured to convert the sum of products from the first format to the second format based on scaling the sum of products with a scaling factor.

There is provided a method and a system for generating clusters of documents using a combined metric parameter. A first document and a second document are received, and for a potential cluster including the first document and the second document: a first metric parameter indicative of a degree of complementariness of document content in the potential cluster is determined, a second metric parameter indicative of a degree of dilution of the document content in the potential cluster is determined. The combined metric parameter is determined based on the first metric parameter and the second metric parameter. A cluster is generated based on the combined metric parameter, where the cluster includes the first and second documents. Other document(s) or clusters may be added to the cluster by determining an updated combined metric parameter for a potential cluster and comparing the updated combined metric parameter with the combined metric parameter.

A computer processor includes a processor cache that obtains tree data from the memory unit indicative of key values that are pre-sorted in a memory unit. A hardware adaptive merge sort accelerator generates a tournament tree based on the key values, and performs a partial tournament sort that compares a selected key value to a plurality of participating key values to define a sorting path. The hardware adaptive merge sort accelerator also determines an overall winning key value of the partial tournament and a runner-up key value located on the sorting path that is a next lowest key value among the participating key values. The remaining key values are compared to the runner-up key value to sort at least one of the remaining key values in sequential order with respect to the overall winning key value and the runner-up key value.