An information processing apparatus includes a processor configured to acquire an additional processing program for performing additional processing in response to a connection of an additional processing apparatus configured to perform the additional processing for a function of the information processing apparatus, and store the acquired additional processing program in the information processing apparatus.

Implementations of this specification include identifying a plurality of transactions to be executed in the blockchain, wherein the transactions are arranged in an execution order, wherein the transactions include one or more smart contract calls to smart contracts each having a whitelist identifying one or more accounts that are authorized to execute the smart contract, and wherein the execution order includes a smart contract call to a smart contract that does not have a whitelist arranged after the plurality of transactions; identifying groups of transactions within the plurality of transactions; instructing nodes of the blockchain network to execute each of the groups of transactions in parallel; determining that the nodes of the blockchain network have completed executing all of the groups of transactions; and in response, instructing the nodes of the blockchain network to execute the smart contract call that does not include a whitelist.

The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. Using virtualized hardware iterators, data for processing by the NN/DNN can be traversed and configured to optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, an iterator controller can generate instructions for execution by the NN/DNN representative of one more desired iterator operation types and to perform one or more iterator operations. Data can be iterated according to a selected iterator operation and communicated to one or more neuron processors of the NN/DD for processing and output to a destination memory. The iterator operations can be applied to various volumes of data (e.g., blobs) in parallel or multiple slices of the same volume.

Technologies for providing efficient migration of services include a server device. The server device includes compute engine circuitry to execute a set of services on behalf of a terminal device and migration accelerator circuitry. The migration accelerator circuitry is to determine whether execution of the services is to be migrated from an edge station in which the present server device is located to a second edge station in which a second server device is located, determine a prioritization of the services executed by the server device, and send, in response to a determination that the services are to be migrated and as a function of the determined prioritization, data utilized by each service to the second server device of the second edge station to migrate the services. Other embodiments are also described and claimed.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for placement and scheduling of radio signal processing dataflow operations. An example method provides a primitive radio signal processing computational dataflow graph that comprises nodes representing operations and directed edges representing data flow. The nodes and directed edges of the primitive radio signal processing computational dataflow graph are partitioned to produce a set of software kernels that, when executed on processing units of a target hardware platform, achieve a specific optimization objective. Runtime resource scheduling, including data placement for individual software kernels in the set of software kernels to efficiently execute operations on the processing units of the target hardware platform. The resources of the processing units in the target hardware platform are then allocated according to the defined runtime resource scheduling.

The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as memory data management of a NN/DNN. Using vector quantization of neuron weight values, the processing of data by neurons can be optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, one or more contiguous segments of weight values can be converted into one or more vectors of arbitrary length and each of the one or more vectors can be assigned an index. The generated indexes can be stored in an exemplary vector quantization lookup table and retrieved by exemplary fast weight lookup hardware at run time on the flyas part of an exemplary data processing function of the NN as part of an inline de-quantization operation to obtain needed one or more neuron weight values.

Method and system are disclosed for data flow control and dynamic access of task queues in a parallel processing system. The method includes storing tasks to be serviced in a plurality of task queues based on assigned priorities of the tasks, where a task queue in the plurality of task queue stores tasks having assigned priorities in a given priority range, determining a usage rate associated with the each task queue in the plurality of task queues, determining a dynamic access rate corresponding to the each task queue based on the assigned priority level and the usage rate associated with the each task queue in the plurality of task queues, traversing the plurality of task queues in order, and selecting a task queue from the plurality of task queues for service based on the dynamic access rate associated with the task queue.

A method for data processing is provided. The method may include: preprocessing initial data to obtain preprocessed data; storing the preprocessed data; receiving a data request made through an application, the data request including information relating to a storage path of contents that are requested; in response to the data request, determining, by a nearby proxy of a first proxy cluster in a first region, whether the contents requested in the data request are cached locally; and in response to a determination that the contents are cached locally, providing, by the nearby proxy, the contents to the application; or in response to a determination that the contents are not cached locally, acquiring, by the nearby proxy, the contents based on the information relating to the storage path of the contents; and providing, by the nearby proxy, the contents to the application.

A method of controlling the sequence of program parts has the following steps: initiated by the occurrence of a first event (E1), executing a first program part (P1) on a first arithmetic logic unit (RW1), wherein the first event (E1) and/or the first program part (P1) is/are assigned a first priority (Py1), and initiated by the occurrence of a second event (E2), interrupting the execution of the first program part (P1) and then executing a second program part (P2) on the first arithmetic logic unit (RW1), wherein the second event (E2) and/or the second program part (P2) is/are assigned a second priority (Py2) that is higher than the first priority (Py1), wherein the first and/or the second program part (P1, P2) is/are a function block in the sense of IEC 61499 or a part thereof or a data transmission initiated thereby.

In an embodiment, an operating system for a computer system assigns each independently-schedulable code sequence to an activity. An activity may thus be associated with a group of related code sequences, such as threads that communicate with each other (whether or not they are part of the same program). When a code sequence is ready to be scheduled and it is not part of the current activity, it may preempt the current activity if the activity for the code sequence is not enabled and is not masked by the enabled activities. Each activity may define which other activities it masks. A flexible scheduling scheme may be devised based on the mask assignments for each activity.