Abstract: A data reuse method based on a convolutional neural network accelerator includes a tile scanning module receiving command information of a command module, the command information comprising a size of a CNN job to be divided into tile blocks; a tile scanning module according to a tile. The size of the tile generates the coordinates of the tile block and sends it to the memory request module; the memory request module generates a memory read request and sends the memory read request to the memory module; the memory module sequentially returns the tile block data to the input activation In the weight buffer unit, the input activation weight buffer unit saves the received tile block data to implement data reuse and transmits the received tile block data to the calculation processing unit PE.

Abstract: Methods, systems, and computing platforms for data communication and testing are disclosed. One or more hardware processors may be configured by machine-readable instructions to: electronically process a plurality of computer executable instructions associated with a plurality of call-to-action code for at least one uniform resource network node in a computer-data communication-based network. The processor(s) may be configured to electronically determine if the call-to-action code is associated with a same container of the at least one uniform resource network node. The one or more hardware processors are further configured by the instructions electronically assign a rank order to the treatment set of call-to-action code.

Abstract: A data reuse method based on a convolutional neural network accelerator includes a tile scanning module receiving command information of a command module, the command information comprising a size of a CNN job to be divided into tile blocks; a tile scanning module according to a tile. The size of the tile generates the coordinates of the tile block and sends it to the memory request module; the memory request module generates a memory read request and sends the memory read request to the memory module; the memory module sequentially returns the tile block data to the input activation In the weight buffer unit, the input activation weight buffer unit saves the received tile block data to implement data reuse and transmits the received tile block data to the calculation processing unit PE.

Abstract: The present invention is a flexible data stream processor and processing method for an artificial intelligence device, including a frontal engine, a parietal engine group, an occipital engine, and a temporal engine; capable of dividing a tensor into a plurality of tile blocks, and then each Tile blocks are divided into several tiles, each tile is divided into several wave blocks, each wave block is divided into several waves, and waves with the same rendering features are processed in the same neuron block; AI work can be distributed across multiple parietal engines for parallel processing and weight reuse, activation reuse, weight station reuse, partial and reuse.