Abstract: A hardware unit for manipulating data stored in a memory comprises an internal buffer, a memory reading block, configured to read the data from the memory and write the data to the internal buffer. a memory writing block, configured to read the data from the internal buffer and write the data to the memory. The hardware unit optionally also comprises a control channel between the memory reading block and the memory writing block, wherein the memory reading block and the memory writing block are configured to communicate via the control channel to maintain synchronisation between them when writing the data to the internal buffer and reading the data from the internal buffer, respectively. The hardware unit may be configured to apply one or more transformations to multidimensional data in the memory. The hardware unit may be configured to traverse the multidimensional array using a plurality of nested loops.

Abstract: The present disclosure relates to moving workloads between cloud providers. A traceability application can receive a request to register a workload from a first virtualization service associated with a first cloud computing environment. To register the workload, the traceability application can generate an identification token in a distributed data store and an asset record corresponding to the identification token. The identification token can uniquely identify the workload among a plurality of workloads associated with a plurality of cloud computing environments. The traceability application can detect a migration of the VM from the first virtualization service associated with the first cloud computing environment to a second virtualization service associated with the second cloud computing environment. The traceability application can cause a transfer of an ownership of the identification token from the first virtualization service to the second virtualization service.

Abstract: A hardware unit for manipulating data stored in a memory comprises an internal buffer, a memory reading block, configured to read the data from the memory and write the data to the internal buffer. a memory writing block, configured to read the data from the internal buffer and write the data to the memory. The hardware unit optionally also comprises a control channel between the memory reading block and the memory writing block, wherein the memory reading block and the memory writing block are configured to communicate via the control channel to maintain synchronisation between them when writing the data to the internal buffer and reading the data from the internal buffer, respectively. The hardware unit may be configured to apply one or more transformations to multidimensional data in the memory. The hardware unit may be configured to traverse the multidimensional array using a plurality of nested loops.

Abstract: A multicore hardware implementation of a deep neural network includes a plurality of layers arranged in plurality of layer groups. The input data to the network comprises a multidimensional tensor including one or more traversed dimensions that are traversed by strides in at least one layer of a first layer group, and one or more non-traversed dimensions. If a size of the input data in a first dimension is greater than a threshold, the hardware implementation splits the input data for the first layer group into at least a first tile and a second tile, along the first dimension. If the size of the input data in the first dimension is not greater than the threshold, the hardware implementation splits the evaluation of the first layer group into at least a first pass and a second pass, along a dimension other than the first dimension.

Abstract: A multicore hardware implementation of a deep neural network includes a plurality of layers arranged in plurality of layer groups. The input data to the network comprises a multidimensional tensor including one or more traversed dimensions, being dimensions that are traversed by strides in at least one layer of a first layer group. The hardware implementation is configured to split the input data for the first layer group into at least a first tile and a second tile, along at least one of the traversed dimensions, each tile comprising a plurality of data elements in each of the one or more traversed dimensions. A first core is configured to evaluate multiple layer groups, depth-first, based on the first tile. A second core is configured to evaluate multiple layer groups, depth-first, based on the second tile.

Abstract: A hardware unit for manipulating data stored in a memory comprises an internal buffer, a memory reading block, configured to read the data from the memory and write the data to the internal buffer. a memory writing block, configured to read the data from the internal buffer and write the data to the memory. The hardware unit optionally also comprises a control channel between the memory reading block and the memory writing block, wherein the memory reading block and the memory writing block are configured to communicate via the control channel to maintain synchronisation between them when writing the data to the internal buffer and reading the data from the internal buffer, respectively. The hardware unit may be configured to apply one or more transformations to multidimensional data in the memory. The hardware unit may be configured to traverse the multidimensional array using a plurality of nested loops.

Abstract: A multicore hardware implementation of a deep neural network includes a plurality of layers arranged in plurality of layer groups. The input data to the network comprises a multidimensional tensor including one or more traversed dimensions, being dimensions that are traversed by strides in at least one layer of a first layer group. The hardware implementation is configured to split the input data for the first layer group into at least a first tile and a second tile, along at least one of the traversed dimensions, each tile comprising a plurality of data elements in each of the one or more traversed dimensions. A first core is configured to evaluate multiple layer groups, depth-first, based on the first tile. A second core is configured to evaluate multiple layer groups, depth-first, based on the second tile.

Abstract: A multicore hardware implementation of a deep neural network includes a plurality of layers arranged in plurality of layer groups. The input data to the network comprises a multidimensional tensor including one or more traversed dimensions that are traversed by strides in at least one layer of a first layer group, and one or more non-traversed dimensions. The hardware implementation splits the evaluation of the first layer group into a first pass and a second pass, along one of the traversed dimensions or one of the non-traversed dimensions. A first core evaluates the first layer group for the first pass, to generate a first portion of output data. A second core evaluates the first layer group for the second pass, to generate a second portion of output data. The hardware implementation combines the first portion of output data and the second portion of output data to produce the output data of the first layer group.

Abstract: A multicore hardware implementation of a deep neural network includes a plurality of layers arranged in plurality of layer groups. The input data to the network comprises a multidimensional tensor including one or more traversed dimensions that are traversed by strides in at least one layer of a first layer group, and one or more non-traversed dimensions. If a size of the input data in a first dimension is greater than a threshold, the hardware implementation splits the input data for the first layer group into at least a first tile and a second tile, along the first dimension. If the size of the input data in the first dimension is not greater than the threshold, the hardware implementation splits the evaluation of the first layer group into at least a first pass and a second pass, along a dimension other than the first dimension.

Abstract: A hardware unit for manipulating data stored in a memory comprises an internal buffer, a memory reading block, configured to read the data from the memory and write the data to the internal buffer. a memory writing block, configured to read the data from the internal buffer and write the data to the memory. The hardware unit optionally also comprises a control channel between the memory reading block and the memory writing block, wherein the memory reading block and the memory writing block are configured to communicate via the control channel to maintain synchronisation between them when writing the data to the internal buffer and reading the data from the internal buffer, respectively. The hardware unit may be configured to apply one or more transformations to multidimensional data in the memory. The hardware unit may be configured to traverse the multidimensional array using a plurality of nested loops.