Processors, systems and methods are provided for thread level parallel processing. A processor may comprise a plurality of processing elements (PEs) each having a plurality of arithmetic logic units (ALUs) that are configured to execute a same instruction in parallel threads and a plurality of memory ports (MPs) for the plurality of PEs to access a memory unit. Each of the plurality of MPs may comprise an address calculation unit configured to generate respective memory addresses for each thread to access a common area in the memory unit.

Processors, systems and methods are provided for thread level parallel processing. A processor may comprise a plurality of reconfigurable units that may include a plurality of processing elements (PEs) and a plurality of memory ports (MPs) for the plurality of PEs to access a memory unit. Each of the plurality of reconfigurable units may comprise a configuration buffer and a reconfiguration counter. The processor may further comprise a sequencer coupled to the configuration buffer of each of the plurality of reconfigurable units and configured to distribute a plurality of configurations to the plurality of reconfigurable units for the plurality of PEs and the plurality of MPs to execute a sequence of instructions.

Providing reconfigurable fusion of processing elements (PEs) in vector-processor-based devices is disclosed. In this regard, a vector-processor-based device provides a vector processor including a plurality of PEs and a decode/control circuit. The decode/control circuit receives an instruction block containing a vectorizable loop comprising a loop body. The decode/control circuit determines how many PEs of the plurality of PEs are required to execute the loop body, and reconfigures the plurality of PEs into one or more fused PEs, each including the determined number of PEs required to execute the loop body. The plurality of PEs, reconfigured into one or more fused PEs, then executes one or more loop iterations of the loop body. Some aspects further include a PE communications link interconnecting the plurality of PEs, to enable communications between PEs of a fused PE and communications of inter-iteration data dependencies between PEs without requiring vector register file access operations.

a Systems and methods are provided for matrix tiling to accelerate computing in redundant matrices. The method may include identifying unique submatrices in the matrix; loading values of elements of each unique submatrix into a respective one of the array processors; applying the vector to inputs of each of the array processors; and adding outputs of the array processors according to locations of the unique submatrices in the matrix.

Systems and methods for automated systolic array design from a high-level program are disclosed. One implementation of a systolic array design supporting a convolutional neural network includes a two-dimensional array of reconfigurable processing elements arranged in rows and columns. Each processing element has an associated SIMD vector and is connected through a local connection to at least one other processing element. An input feature map buffer having a double buffer is configured to store input feature maps, and an interconnect system is configured to pass data to neighboring processing elements in accordance with a processing element scheduler. A CNN computation is mapped onto the two-dimensional array of reconfigurable processing elements using an automated system configured to determine suitable reconfigurable processing element parameters.

Processors, systems and methods are provided for thread level parallel processing. A processor may comprise a plurality of processing elements (PEs) and a plurality of memory ports (MPs) for the plurality of PEs to access a memory unit. Each PE may have a plurality of arithmetic logic units (ALUs) that are configured to execute a same instruction in parallel threads. Each of the plurality of MPs may comprise an address calculation unit configured to generate respective memory addresses for each thread to access a different memory bank in the memory unit.

Processors, systems and methods are provided for thread level parallel processing. A processor may comprise a plurality of processing elements (PEs) that each may comprise a configuration buffer, a sequencer coupled to the configuration buffer of each of the plurality of PEs and configured to distribute one or more PE configurations to the plurality of PEs, and a gasket memory coupled to the plurality of PEs and being configured to store at least one PE execution result to be used by at least one of the plurality of PEs during a next PE configuration.

Processors, systems and methods are provided for thread level parallel processing. A processor may comprise a plurality of reconfigurable units that may include a plurality of processing elements (PEs) and a plurality of memory ports (MPs) for the plurality of PEs to access a memory unit. Each of the plurality of reconfigurable units may comprise a configuration buffer and a reconfiguration counter. The processor may further comprise a sequencer coupled to the configuration buffer of each of the plurality of reconfigurable units and configured to distribute a plurality of configurations to the plurality of reconfigurable units for the plurality of PEs and the plurality of MPs to execute a sequence of instructions.

Processors, systems and methods are provided for thread level parallel processing. A processor may comprise a plurality of processing elements (PEs) each having a plurality of arithmetic logic units (ALUs) that are configured to execute a same instruction in parallel threads and a plurality of memory ports (MPs) for the plurality of PEs to access a memory unit. Each of the plurality of MPs may comprise an address calculation unit configured to generate respective memory addresses for each thread to access a common area in the memory unit.

Systems and methods are provided for matrix tiling to accelerate computing in redundant matrices. The method may include identifying unique submatrices in the matrix; loading values of elements of each unique submatrix into a respective one of the array processors; applying the vector to inputs of each of the array processors; and adding outputs of the array processors according to locations of the unique submatrices in the matrix.