Circuits and associated methods for processing two floating-point numbers (A, B) to generate a sum (A+B) of the two numbers and a difference (A−B) of the two numbers include calculating (806) a sum (|A|+|B|) of the absolute values of the two floating-point numbers, using a same-sign floating-point adder (1020), to produce a first result. The method further comprises calculating (808) a difference (|A|−|B|) of the absolute values to produce a second result. The sum (A+B) and the difference (A−B) are generated (810, 812) based on the first result (|A|+|B|), the second result (|A|−|B|), and the sign of each floating-point number.

A processing-in-memory (PIM) system includes a PIM device and a controller. The PIM device includes a data storage region and an arithmetic circuit for performing an arithmetic operation for data outputted from the data storage region. The controller is configured to control the PIM device. The PIM device is configured to transmit arithmetic quantity data of the arithmetic circuit to the controller in response to a request of the controller.

A processing-in-memory (PIM) system includes a PIM device and a controller. The PIM device includes a data storage region and an arithmetic circuit for performing an arithmetic operation for data outputted from the data storage region. The controller is configured to control the PIM device. The PIM device is configured to transmit arithmetic quantity data of the arithmetic circuit to the controller in response to a request of the controller.

Various embodiments of the disclosure relate to an accelerated mathematical engine. In certain embodiments, the accelerated mathematical engine is applied to image processing such that convolution of an image is accelerated by using a two-dimensional matrix processor comprising sub-circuits that include an ALU, output register and shadow register. This architecture supports a clocked, two-dimensional architecture in which image data and weights are multiplied in a synchronized manner to allow a large number of mathematical operations to be performed in parallel.

Various embodiments of the disclosure relate to an accelerated mathematical engine. In certain embodiments, the accelerated mathematical engine is applied to image processing such that convolution of an image is accelerated by using a two-dimensional matrix processor comprising sub-circuits that include an ALU, output register and shadow register. This architecture supports a clocked, two-dimensional architecture in which image data and weights are multiplied in a synchronized manner to allow a large number of mathematical operations to be performed in parallel.

Pathways between reference locations in a physical system are generated based on a layout table. Nodes and edges of the directed graph are associated with cell locations of the layout table. The cell locations define features of the reference locations. Parameters of the nodes and edges are defined based on descriptors recalled from the cells associated with the nodes and edges. The nodes and edges are configured based on the descriptors. Path data regarding potential pathways is generated based on the defined nodes and edges.

An DNN accelerator may perform 1×N kernel decomposition to decompose a convolutional kernel into kernel vectors, each of which includes multiple weights. Through the kernel decomposition, a weight operand may be generated from a filter. The DNN accelerator converts an input tensor into input operands. An input operand includes activations and has the same size as the weight operand. The DNN accelerator may read a first activation in the input operand from memory to an internal memory of a first PE and read a second activation in the input operand from the memory to an internal memory of a second PE. The first PE may receive the second activation from the second PE through activation broadcasting between the two PEs and perform MAC operations on the input operand and weight operand. The second PE may perform MAC operations on another input operand in the input tensor and the weight operand.

An DNN accelerator may perform 1×N kernel decomposition to decompose a convolutional kernel into kernel vectors, each of which includes multiple weights. Through the kernel decomposition, a weight operand may be generated from a filter. The DNN accelerator converts an input tensor into input operands. An input operand includes activations and has the same size as the weight operand. The DNN accelerator may read a first activation in the input operand from memory to an internal memory of a first PE and read a second activation in the input operand from the memory to an internal memory of a second PE. The first PE may receive the second activation from the second PE through activation broadcasting between the two PEs and perform MAC operations on the input operand and weight operand. The second PE may perform MAC operations on another input operand in the input tensor and the weight operand.

A 2D array-based neuromorphic processor includes: axon circuits each being configured to receive a first input corresponding to one bit from among bits indicating n-bit activation; first direction lines extending in a first direction from the axon circuits; second direction lines intersecting the first direction lines; synapse circuits disposed at intersections of the first direction lines and the second direction lines, and each being configured to store a second input corresponding to one bit from among bits indicating an m-bit weight and to output operation values of the first input and the second input; and neuron circuits connected to the first or second direction lines, each of the neuron circuits being configured to receive an operation value output from at least one of the synapse circuits, based on time information assigned individually to the synapse circuits, and to perform an arithmetic operation by using the operation values.

A 2D array-based neuromorphic processor includes: axon circuits each being configured to receive a first input corresponding to one bit from among bits indicating n-bit activation; first direction lines extending in a first direction from the axon circuits; second direction lines intersecting the first direction lines; synapse circuits disposed at intersections of the first direction lines and the second direction lines, and each being configured to store a second input corresponding to one bit from among bits indicating an m-bit weight and to output operation values of the first input and the second input; and neuron circuits connected to the first or second direction lines, each of the neuron circuits being configured to receive an operation value output from at least one of the synapse circuits, based on time information assigned individually to the synapse circuits, and to perform an arithmetic operation by using the operation values.