Abstract: A multi-accumulator multiply-and-accumulate (MAC) unit can include a multiplier and a plurality of accumulators. The multiplier can be configured to multiply a given element of a corresponding column of a first matrix and a plurality of elements of a corresponding row of a second matrix to generate a plurality of corresponding partial product elements that can be accumulated by corresponding ones of the plurality of accumulators.

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 memory regions. The plurality of memory regions can be organized in a plurality of memory blocks. The plurality of memory regions can be configured to store integer, B-float, and/or Group B-float encode data. The plurality of processing regions can be interleaved between the plurality of processing regions of the first memory. The plurality of processing regions can be organized in a plurality of core groups include a plurality of compute cores. The compute groups in the processing regions can be coupled to a plurality of adjacent memory blocks in the adjacent memory regions. The second memory can be coupled to the plurality of 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 memory regions. The plurality of memory regions can be organized in a plurality of memory blocks. The plurality of processing regions can be interleaved between the plurality of processing regions of the first memory. The plurality of processing regions can be organized in a plurality of core groups include a plurality of compute cores. The compute groups in the processing regions can be coupled to a plurality of adjacent memory blocks in the adjacent memory regions. The second memory can be coupled to the plurality of 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 computing system can include an off-chip memory and processing unit integrated circuitry. The processing unit IC can include on-chip compute circuitry, a first on-chip memory and a second on-chip memory. The off-chip memory can be configured to store instructions and data The first on-chip memory can be configured to store reusable portions of the instructions and or data for use by the on-chip compute circuitry. The second on-chip memory configured to cache portions of instruction and data for current use by the on-chip compute circuitry.

Abstract: A method is presented for mapping weights for kernels of a neural network onto a crossbar array. In one example, the crossbar array is comprised of an array of non-volatile memory cells arranged in columns and rows, such that memory cells in each row of the array is interconnected by a respective drive line and each column of the array is interconnected by a respective bit line; and wherein each memory cell is configured to receive an input signal indicative of a multiplier and operates to output a product of the multiplier and a weight of the given memory cell onto the corresponding bit line of the given memory cell, where the value of the multiplier is encoded in the input signal and the weight of the given memory cell is stored by the given memory cell.

Abstract: A computing system architecture is presented for decoupling execution of workload by crossbar arrays and similar memory modules. The computing system includes: a data bus; a core controller connected to the data bus; and a plurality of local tiles connected to the data bus. Each local tile in the plurality of local tiles includes a local controller and at least one memory module, where the memory module performs computation using the data stored in memory without reading the data out of the memory.

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 processing unit can include a plurality of chiplets coupled in a cascade topology by a plurality of interfaces. A set of the plurality of cascade coupled chiplets can be configured to execute a plurality of layers or blocks of layers of an artificial intelligence model. The set of cascade coupled chiplets can also be configured with parameter data of corresponding ones of the plurality of layers or blocks of layers of the artificial intelligence model.

Abstract: Many signal processing, machine learning and scientific computing applications require a large number of multiply-accumulate (MAC) operations. This type of operation is demanding in both computation and memory. Process in memory has been proposed as a new technique that computes directly on a large array of data in place, to eliminate expensive data movement overhead. To enable parallel multi-bit MAC operations, both width- and level-modulating memory word lines are applied. To improve performance and provide tolerance against process-voltage-temperature variations, a delay-locked loop is used to generate fine unit pulses for driving memory word lines and a dual-ramp Single-slope ADC is used to convert bit line outputs. The concept is prototyped in a 180 nm CMOS test chip made of four 320×64 compute-SRAMs, each supporting 128× parallel 5 b×5 b MACs with 32 5 b output ADCs and consuming 16.6 mW at 200 MHz.

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 sparse video inference chip is designed to extract spatio-temporal features from videos for action classification and motion tracking. The core is a sparse video inference processor that implements recurrent neural network in three layers of processing. High sparsity is enforced in each layer of processing, reducing the complexity by two orders of magnitude and allowing all multiply-accumulates (MAC) to be replaced by select-accumulates (SA). The design is demonstrated in a 3.98 mm2 40 nm CMOS chip with an Open-RISC processor providing software-defined control and classification.

Abstract: An information processor is provided that includes an inference module configured to extract a subset of data from information in an input and a classification module configured to classify the information in the input based on the extracted data. The inference module includes a first plurality of convolvers acting in parallel to apply each of N1 convolution kernels to each of N2 portions of the input image in order to generate an interim sparse representation of the input and a second plurality of convolvers acting in parallel to apply each of N3 convolution kernels to each of N4 portions of the interim sparse representation to generate a final sparse representation containing the extracted data. In order to take advantage of sparsity in the interim sparse representation, N3 is greater than N4 to parallelize processing in a non-sparse dimension and/or the second plurality of convolvers comprise sparse convolvers.

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: Techniques for computing matrix convolutions in a plurality of multiply and accumulate units including data reuse of adjacent values. The data reuse can include reading a current value of the first matrix in from memory for concurrent use by the plurality of multiply and accumulate units. The data reuse can also include reading a current value of the second matrix in from memory to a serial shift buffer coupled to the plurality of multiply and accumulate units. The data reuse can also include reading a current value of the second matrix in from memory for concurrent use by the plurality of multiply and accumulate units.

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 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.

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.

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.