A neural processing unit (NPU) includes a controller including a scheduler, the controller configured to receive from a compiler a machine code of an artificial neural network (ANN) including a fusion ANN, the machine code including data locality information of the fusion ANN, and receive heterogeneous sensor data from a plurality of sensors corresponding to the fusion ANN; at least one processing element configured to perform fusion operations of the fusion ANN including a convolution operation and at least one special function operation; a special function unit (SFU) configured to perform a special function operation of the fusion ANN; and an on-chip memory configured to store operation data of the fusion ANN, wherein the schedular is configured to control the at least one processing element and the on-chip memory such that all operations of the fusion ANN are processed in a predetermined sequence according to the data locality information.

Technologies for providing streamlined provisioning of accelerated functions in a disaggregated architecture include a compute sled. The compute sled includes a network interface controller and circuitry to determine whether to accelerate a function of a workload executed by the compute sled, and send, to a memory sled and in response to a determination to accelerate the function, a data set on which the function is to operate. The circuitry is also to receive, from the memory sled, a service identifier indicative of a memory location independent handle for data associated with the function, send, to a compute device, a request to schedule acceleration of the function on the data set, receive a notification of completion of the acceleration of the function, and obtain, in response to receipt of the notification and using the service identifier, a resultant data set from the memory sled. The resultant data set was produced by an accelerator device during acceleration of the function on the data set. Other embodiments are also described and claimed.

Technologies for providing streamlined provisioning of accelerated functions in a disaggregated architecture include a compute sled. The compute sled includes a network interface controller and circuitry to determine whether to accelerate a function of a workload executed by the compute sled, and send, to a memory sled and in response to a determination to accelerate the function, a data set on which the function is to operate. The circuitry is also to receive, from the memory sled, a service identifier indicative of a memory location independent handle for data associated with the function, send, to a compute device, a request to schedule acceleration of the function on the data set, receive a notification of completion of the acceleration of the function, and obtain, in response to receipt of the notification and using the service identifier, a resultant data set from the memory sled. The resultant data set was produced by an accelerator device during acceleration of the function on the data set. Other embodiments are also described and claimed.

A neural processing unit (NPU) includes a controller including a scheduler, the controller configured to receive from a compiler a machine code of an artificial neural network (ANN) including a fusion ANN, the machine code including data locality information of the fusion ANN, and receive heterogeneous sensor data from a plurality of sensors corresponding to the fusion ANN; at least one processing element configured to perform fusion operations of the fusion ANN including a convolution operation and at least one special function operation; a special function unit (SFU) configured to perform a special function operation of the fusion ANN; and an on-chip memory configured to store operation data of the fusion ANN, wherein the schedular is configured to control the at least one processing element and the on-chip memory such that all operations of the fusion ANN are processed in a predetermined sequence according to the data locality information.

A neural processing unit (NPU) includes a controller including a scheduler, the controller configured to receive from a compiler a machine code of an artificial neural network (ANN) including a fusion ANN, the machine code including data locality information of the fusion ANN, and receive heterogeneous sensor data from a plurality of sensors corresponding to the fusion ANN; at least one processing element configured to perform fusion operations of the fusion ANN including a convolution operation and at least one special function operation; a special function unit (SFU) configured to perform a special function operation of the fusion ANN; and an on-chip memory configured to store operation data of the fusion ANN, wherein the schedular is configured to control the at least one processing element and the on-chip memory such that all operations of the fusion ANN are processed in a predetermined sequence according to the data locality information.

In an acceleration processing method, an acceleration processing device combines a first acceleration application and a second acceleration application to obtain a first combined application, and burns the first combined application to a first acceleration resource, where the first combined application includes a top module, the first acceleration application, and the second acceleration application, and the top module includes a statement used to invoke the first acceleration application and a statement used to invoke the second acceleration application.

In an acceleration processing method, an acceleration processing device combines a first acceleration application and a second acceleration application to obtain a first combined application, and burns the first combined application to a first acceleration resource, where the first combined application includes a top module, the first acceleration application, and the second acceleration application, and the top module includes a statement used to invoke the first acceleration application and a statement used to invoke the second acceleration application.

A device may receive information identifying a set of tasks to be executed by a microservices application that includes a plurality of microservices. The device may determine an execution time of the set of tasks based on a set of parameters and a model. The set of parameters may include a first parameter that identifies a first number of instances of a first microservice of the plurality of microservices, and a second parameter that identifies a second number of instances of a second microservice of the plurality of microservices. The device may compare the execution time and a threshold. The threshold may be associated with a service level agreement. The device may selectively adjust the first number of instances or the second number of instances based on comparing the execution time and the threshold.

A device may receive information identifying a set of tasks to be executed by a microservices application that includes a plurality of microservices. The device may determine an execution time of the set of tasks based on a set of parameters and a model. The set of parameters may include a first parameter that identifies a first number of instances of a first microservice of the plurality of microservices, and a second parameter that identifies a second number of instances of a second microservice of the plurality of microservices. The device may compare the execution time and a threshold. The threshold may be associated with a service level agreement. The device may selectively adjust the first number of instances or the second number of instances based on comparing the execution time and the threshold.

Various embodiments provide an electronic device and a method, the electronic device comprising: a memory; a first processor; a second processor which has attributes different from those of the first processor; and a control unit, wherein the control unit is configured to identify a task loaded into the memory, select which of the first processor and the second processor is to execute the task, on the basis of attribute information corresponding to a user interaction associated with the task, and allocate the task to the selected processor. Other embodiments are also possible.