Abstract: A memory processing unit (MPU) can include a first memory, a second memory, a plurality of processing regions and control logic. The first memory can include a plurality of regions. The plurality of processing regions can be interleaved between the plurality of regions of the first memory. The processing regions can include a plurality of compute cores. The second memory can be coupled to the plurality of processing regions. The control logic can configure data flow between compute cores of one or more of the processing regions and corresponding adjacent regions of the first memory. The control logic can also configure data flow between the second memory and the compute cores of one or more of the processing regions. The control logic can also configure data flow between compute cores within one or more respective ones of the processing regions.

Abstract: A memory processing unit (MPU) can include a first memory, a second memory, a plurality of processing regions and control logic. The first memory can include a plurality of regions. The plurality of processing regions can be interleaved between the plurality of regions of the first memory. The processing regions can include a plurality of compute cores. The second memory can be coupled to the plurality of processing regions. The control logic can configure data flow between compute cores of one or more of the processing regions and corresponding adjacent regions of the first memory. The control logic can also configure data flow between the second memory and the compute cores of one or more of the processing regions. The control logic can also configure data flow between compute cores within one or more respective ones of the processing regions.

Abstract: A matrix multiplication engine can include a plurality of processing elements configured to compute a matrix dot product as a summation of a sequence of vector-vector outer-products.

Abstract: A memory processing unit (MPU) configuration method can include mapping operations of one or more neural network models to sets of cores in a plurality of processing regions. In addition, dataflow of the one or more neural network models can be mapped to the sets of cores in the plurality of processing regions. Furthermore, configuration information can be generated based on the mapping of the operations of the one or more neural network models to the set of cores in the plurality of processing regions and the mapping of dataflow of the one or more neural network models to the sets of cores in the plurality of processing regions. The method can be implemented by generating an initial graph from a neural network model. A mapping graph can then be generated from the final graph. One or more configuration files can then be generated from the mapping graph.

Abstract: A monolithic integrated circuit (IC) including one or more compute circuitry, one or more non-volatile memory circuits, one or more communication channels and one or more communication interface. The one or more communication channels can communicatively couple the one or more compute circuitry, the one or more non-volatile memory circuits and the one or more communication interface together. The one or more communication interfaces can communicatively couple one or more circuits of the monolithic integrated circuit to one or more circuits external to the monolithic integrated circuit.

Abstract: A memory processing unit architecture can include a plurality of memory regions and a plurality of processing regions interleaved between the plurality of memory regions. The plurality of processing regions can be configured to perform computation functions of a model such as an artificial neural network. Data can be transferred between the computation functions in respective memory processing regions. In addition, the memory regions can be utilized to transfer data between a computation function in one processing region and a computation function in another processing region adjacent to the given memory region.

Abstract: A memory processing unit can be configured to compute partial products between one or more elements of a first matrix stored in a given row of a memory cell array and sequential bits of one or more elements of a second matrix. The partial products can be calculated first sequentially across the set of rows and second sequentially across the bit positions of the elements of the second matrix. Alternatively, the partial products can be calculated first sequentially across the bit positions of the elements of the second matrix first and second sequentially across the set of rows. The partial products for each column of elements can be accumulated and bit shifted to compute the dot product of the first and second matrix.

Abstract: A memory processing unit can be configured to compute partial products between one or more elements of a first matrix stored in a first storage location and sequential bits of one or more elements of a second matrix stored in a second storage location. The partial products can be calculated utilizing zero bit skipping to increase throughput and or reduce energy consumption. The partial products for each column of elements can be accumulated and bit shifted to compute the dot product of the first and second matrix.