A matrix multiplier and an operation method thereof are provided. The matrix multiplier includes a plurality of first input lines, a plurality of second input lines and a computing array. The computing array includes a plurality of multiplication accumulation (MAC) cells. A first MAC cell of the plurality of MAC cells is coupled to a first corresponding input line of the plurality of first input lines and a second corresponding input line of the plurality of second input lines to receive a first input value and a second input value to perform a multiplication accumulation operation. When at least one of the first input value and the second input value is a specified value, the multiplication accumulation operation of the first MAC cell is disabled.

A matrix multiplier and an operation method thereof are provided. The matrix multiplier includes a plurality of first input lines, a plurality of second input lines and a computing array. The computing array includes a plurality of multiplication accumulation (MAC) cells. A first MAC cell of the plurality of MAC cells is coupled to a first corresponding input line of the plurality of first input lines and a second corresponding input line of the plurality of second input lines to receive a first input value and a second input value to perform a multiplication accumulation operation. When at least one of the first input value and the second input value is a specified value, the multiplication accumulation operation of the first MAC cell is disabled.

To improve efficiencies for inferencing operations of neural networks, ensemble neural networks are used for compute-in-memory inferencing. In an ensemble neural network, the layers of a neural network are replaced by an ensemble of multiple smaller neural network generated from subsets of the same training data as would be used for the layers of the full neural network. Although the individual smaller network layers are “weak classifiers” that will be less accurate than the full neural network, by combining their outputs, such as in majority voting or averaging, the ensembles can have accuracies approaching that of the full neural network. Ensemble neural networks for compute-in-memory operations can have their efficiency further improved by implementations based binary memory cells, such as by binary neural networks using binary valued MRAM memory cells. The size of an ensemble can be increased or decreased to optimize the system according to error requirements.

To improve efficiencies for inferencing operations of neural networks, ensemble neural networks are used for compute-in-memory inferencing. In an ensemble neural network, the layers of a neural network are replaced by an ensemble of multiple smaller neural network generated from subsets of the same training data as would be used for the layers of the full neural network. Although the individual smaller network layers are “weak classifiers” that will be less accurate than the full neural network, by combining their outputs, such as in majority voting or averaging, the ensembles can have accuracies approaching that of the full neural network. Ensemble neural networks for compute-in-memory operations can have their efficiency further improved by implementations based binary memory cells, such as by binary neural networks using binary valued MRAM memory cells. The size of an ensemble can be increased or decreased to optimize the system according to error requirements.

The present disclosure includes apparatuses and methods related to performing logical operations using sensing circuitry. An example apparatus comprises an array of memory cells and sensing circuitry comprising a primary latch coupled to a sense line of the array. The sensing circuitry can be configured to perform a first operation phase of a logical operation by sensing a memory cell coupled to the sense line, perform a number of intermediate operation phases of the logical operation by sensing a respective number of different memory cells coupled to the sense line, and accumulate a result of the first operation phase and the number of intermediate operation phases in a secondary latch coupled to the primary latch without performing a sense line address access.

The present disclosure includes apparatuses and methods related to performing logical operations using sensing circuitry. An example apparatus comprises an array of memory cells and sensing circuitry comprising a primary latch coupled to a sense line of the array. The sensing circuitry can be configured to perform a first operation phase of a logical operation by sensing a memory cell coupled to the sense line, perform a number of intermediate operation phases of the logical operation by sensing a respective number of different memory cells coupled to the sense line, and accumulate a result of the first operation phase and the number of intermediate operation phases in a secondary latch coupled to the primary latch without performing a sense line address access.

The present disclosure describes a digital signal processing (DSP) block that includes a plurality of columns of weight registers and a plurality of inputs configured to receive a first plurality of values and a second plurality of values. The first plurality of values is stored in the plurality of columns of weight registers after being received. Additionally, the DSP block includes a plurality of multipliers configured to simultaneously multiply each value of the first plurality of values by each value of the second plurality of values.

The present disclosure describes a digital signal processing (DSP) block that includes a plurality of columns of weight registers and a plurality of inputs configured to receive a first plurality of values and a second plurality of values. The first plurality of values is stored in the plurality of columns of weight registers after being received. Additionally, the DSP block includes a plurality of multipliers configured to simultaneously multiply each value of the first plurality of values by each value of the second plurality of values.

A representative reconfigurable processing circuit and a reconfigurable arithmetic circuit are disclosed, each of which may include input reordering queues; a multiplier shifter and combiner network coupled to the input reordering queues; an accumulator circuit; and a control logic circuit, along with a processor and various interconnection networks. A representative reconfigurable arithmetic circuit has a plurality of operating modes, such as floating point and integer arithmetic modes, logical manipulation modes, Boolean logic, shift, rotate, conditional operations, and format conversion, and is configurable for a wide variety of multiplication modes. Dedicated routing connecting multiplier adder trees allows multiple reconfigurable arithmetic circuits to be reconfigurably combined, in pair or quad configurations, for larger adders, complex multiplies and general sum of products use, for example.

A representative reconfigurable processing circuit and a reconfigurable arithmetic circuit are disclosed, each of which may include input reordering queues; a multiplier shifter and combiner network coupled to the input reordering queues; an accumulator circuit; and a control logic circuit, along with a processor and various interconnection networks. A representative reconfigurable arithmetic circuit has a plurality of operating modes, such as floating point and integer arithmetic modes, logical manipulation modes, Boolean logic, shift, rotate, conditional operations, and format conversion, and is configurable for a wide variety of multiplication modes. Dedicated routing connecting multiplier adder trees allows multiple reconfigurable arithmetic circuits to be reconfigurably combined, in pair or quad configurations, for larger adders, complex multiplies and general sum of products use, for example.