A system to support a machine learning (ML) operation comprises a core configured to receive and interpret commands into a set of instructions for the ML operation and a memory unit configured to maintain data for the ML operation. The system further comprises an inference engine having a plurality of processing tiles, each comprising an on-chip memory (OCM) configured to maintain data for local access by components in the processing tile and one or more processing units configured to perform tasks of the ML operation on the data in the OCM. The system also comprises an instruction streaming engine configured to distribute the instructions to the processing tiles to control their operations and to synchronize data communication between the core and the inference engine so that data transmitted between them correctly reaches the corresponding processing tiles while ensuring coherence of data shared and distributed among the core and the OCMs.

A system to support a machine learning (ML) operation comprises a core configured to receive and interpret commands into a set of instructions for the ML operation and a memory unit configured to maintain data for the ML operation. The system further comprises an inference engine having a plurality of processing tiles, each comprising an on-chip memory (OCM) configured to maintain data for local access by components in the processing tile and one or more processing units configured to perform tasks of the ML operation on the data in the OCM. The system also comprises an instruction streaming engine configured to distribute the instructions to the processing tiles to control their operations and to synchronize data communication between the core and the inference engine so that data transmitted between them correctly reaches the corresponding processing tiles while ensuring coherence of data shared and distributed among the core and the OCMs.

The present disclosure provides a data read-write scheduler and a reservation station for vector operations. The data read-write scheduler suspends the instruction execution by providing a read instruction cache module and a write instruction cache module and detecting conflict instructions based on the two modules. After the time is satisfied, instructions are re-executed, thereby solving the read-after-write conflict and the write-after-read conflict between instructions and guaranteeing that correct data are provided to a vector operations component. Therefore, the subject disclosure has more values for promotion and application.

The present invention is directed to the storage technical field and discloses an on-chip data partitioning read-write method, the method comprises: a data partitioning step for storing on-chip data in different areas, and storing the on-chip data in an on-chip storage medium and an off-chip storage medium respectively, based on a data partitioning strategy; a pre-operation step for performing an operational processing of an on-chip address index of the on-chip storage data in advance when implementing data splicing; and a data splicing step, for splicing the on-chip storage data and the off-chip input data to obtain a representation of the original data based on a data splicing strategy. Also provided are a corresponding on-chip data partitioning read-write system and device. Thus, read and write of repeated data can be efficiently realized, reducing memory access bandwidth requirements while providing good flexibility, thus reducing on-chip storage overhead.

The present disclosure provides a data read-write scheduler and a reservation station for vector operations. The data read-write scheduler suspends the instruction execution by providing a read instruction cache module and a write instruction cache module and detecting conflict instructions based on the two modules. After the time is satisfied, instructions are re-executed, thereby solving the read-after-write conflict and the write-after-read conflict between instructions and guaranteeing that correct data are provided to a vector operations component. Therefore, the subject disclosure has more values for promotion and application.

A system to support a machine learning (ML) operation comprises an array-based inference engine comprising a plurality of processing tiles each comprising at least one or more of an on-chip memory (OCM) configured to maintain data for local access by components in the processing tile and one or more processing units configured to perform one or more computation tasks on the data in the OCM by executing a set of task instructions. The system also comprises a data streaming engine configured to stream data between a memory and the OCMs and an instruction streaming engine configured to distribute said set of task instructions to the corresponding processing tiles to control their operations and to synchronize said set of task instructions to be executed by each processing tile, respectively, to wait current certain task at each processing tile to finish before starting a new one.

A multi-core processor providing heterogeneous processor cores and a shared cache is presented.

Methods, systems, and apparatus, including computer-readable media, are described for an integrated circuit that accelerates machine-learning computations. The circuit includes processor cores that each include: multiple channel controllers; an interface controller for coupling each channel controller to any memory channel of a system memory; and a fetch unit in each channel controller. Each fetch is configured to: receive channel data that encodes addressing information; obtain, based on the addressing information, data from any memory channel of the system memory using the interface controller; and write the obtained data to a vector memory of the processor core via the corresponding channel controller that includes the respective fetch unit.

A multi-core processor providing heterogeneous processor cores and a shared cache is presented.

The present disclosure provides a data read-write scheduler and a reservation station for vector operations. The data read-write scheduler suspends the instruction execution by providing a read instruction cache module and a write instruction cache module and detecting conflict instructions based on the two modules. After the time is satisfied, instructions are re-executed, thereby solving the read-after-write conflict and the write-after-read conflict between instructions and guaranteeing that correct data are provided to a vector operations component. Therefore, the subject disclosure has more values for promotion and application.