Abstract: A network of matrix processing units (MPUs) is provided on a device, where each MPU is connected to at least one other MPU in the network, and each MPU is to perform matrix multiplication operations. Computer memory stores tensor data and a master control central processing unit (MCC) is provided on the device to receive an instruction from a host device, where the instruction includes one or more tensor operands based on the tensor data. The MCC invokes a set of operations on one or more of the MPUs based on the instruction, where the set of operations includes operations on the tensor operands. A result is generated from the set of operations, the result embodied as a tensor value.

Abstract: In one embodiment, a matrix operation may be performed using a plurality of input matrices, wherein the matrix operation is associated with one or more convolution operations. The plurality of input matrices may be partitioned into a plurality of input partitions, wherein the plurality of input matrices is partitioned based on a number of available processing elements. The plurality of input partitions may be distributed among a plurality of processing elements, wherein each input partition is distributed to a particular processing element of the plurality of processing elements. A plurality of partial matrix operations may be performed using the plurality of processing elements, and partial matrix data may be transmitted between the plurality of processing elements while performing the plurality of partial matrix operations. A result of the matrix operation may be determined based on the plurality of partial matrix operations.

Abstract: A network of matrix processing units (MPUs) is provided on a device, where each MPU is connected to at least one other MPU in the network, and each MPU is to perform matrix multiplication operations. Computer memory stores tensor data and a master control central processing unit (MCC) is provided on the device to receive an instruction from a host device, where the instruction includes one or more tensor operands based on the tensor data. The MCC invokes a set of operations on one or more of the MPUs based on the instruction, where the set of operations includes operations on the tensor operands. A result is generated from the set of operations, the result embodied as a tensor value.

Abstract: A network of matrix processing units (MPUs) is provided on a device, where each MPU is connected to at least one other MPU in the network, and each MPU is to perform matrix multiplication operations. Computer memory stores tensor data and a master control central processing unit (MCC) is provided on the device to receive an instruction from a host device, where the instruction includes one or more tensor operands based on the tensor data. The MCC invokes a set of operations on one or more of the MPUs based on the instruction, where the set of operations includes operations on the tensor operands. A result is generated from the set of operations, the result embodied as a tensor value.

Abstract: In one embodiment, a matrix operation may be performed using a plurality of input matrices, wherein the matrix operation is associated with one or more convolution operations. The plurality of input matrices may be partitioned into a plurality of input partitions, wherein the plurality of input matrices is partitioned based on a number of available processing elements. The plurality of input partitions may be distributed among a plurality of processing elements, wherein each input partition is distributed to a particular processing element of the plurality of processing elements. A plurality of partial matrix operations may be performed using the plurality of processing elements, and partial matrix data may be transmitted between the plurality of processing elements while performing the plurality of partial matrix operations. A result of the matrix operation may be determined based on the plurality of partial matrix operations.

Abstract: Embodiments of the present disclosure are directed toward techniques and apparatus comprising at least one layer of an ONN that includes an optical matrix multiplier provided in a semiconductor substrate to receive a plurality of optical signal inputs and to linearly transform the plurality of optical signal inputs into a plurality of optical signal outputs. The optical matrix multiplier comprises one or more 2×2 unitary optical matrices optically interconnected to implement a singular value decomposition (SVD) of a matrix, and a nonlinear optical device coupled with the optical matrix multiplier in the semiconductor substrate, to receive the optical signal outputs and to provide an optical output that is generated in a nonlinear manner in response to the optical signal outputs of the optical matrix multiplier reaching saturation or attenuation. Additional embodiments may be described and claimed.

Abstract: Embodiments of the present disclosure are directed toward techniques and apparatus comprising at least one layer of an ONN that includes an optical matrix multiplier provided in a semiconductor substrate to receive a plurality of optical signal inputs and to linearly transform the plurality of optical signal inputs into a plurality of optical signal outputs. The optical matrix multiplier comprises one or more 2×2 unitary optical matrices optically interconnected to implement a singular value decomposition (SVD) of a matrix, and a nonlinear optical device coupled with the optical matrix multiplier in the semiconductor substrate, to receive the optical signal outputs and to provide an optical output that is generated in a nonlinear manner in response to the optical signal outputs of the optical matrix multiplier reaching saturation or attenuation. Additional embodiments may be described and claimed.

Abstract: Embodiments of the present disclosure are directed toward techniques and configurations for a photonics integrated circuit (IC) for an optical neural network (ONN). In embodiments, the photonics IC includes monolithically optoelectronic components in a single semiconductor substrate including a combination of one or more of integrated array of light sources, a plurality of optical modulators, an optical unitary matrix multiplier, non-linear optical amplifiers or attenuators, and a plurality of photodetectors. In embodiments, the optical unitary matrix multiplier comprises a plurality of 2×2 unitary optical matrices optically interconnected, wherein each 2×2 unitary optical matrix comprises a plurality of phase shifters. In embodiments, each 2×2 unitary optical matrix is to phase shift, split, and/or combine one or more of the optical signal inputs. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure are directed toward techniques and apparatus comprising at least one layer of an ONN that includes an optical matrix multiplier provided in a semiconductor substrate to receive a plurality of optical signal inputs and to linearly transform the plurality of optical signal inputs into a plurality of optical signal outputs. The optical matrix multiplier comprises one or more 2×2 unitary optical matrices optically interconnected to implement a singular value decomposition (SVD) of a matrix, and a nonlinear optical device coupled with the optical matrix multiplier in the semiconductor substrate, to receive the optical signal outputs and to provide an optical output that is generated in a nonlinear manner in response to the optical signal outputs of the optical matrix multiplier reaching saturation or attenuation. Additional embodiments may be described and claimed.

Abstract: Techniques and configurations for an optical neural network (ONN) with layers of optical matrix multipliers and an optical nonlinearity function are described herein. The techniques provide for programmable matrix multipliers, allowing for a partitioned use of a part of a matrix as needed, for computation efficiency. The techniques provide for multiple pass-through the same optical matrix die on the same photonic integrated circuit (PIC) chip and for connecting multiple layers of the ONN and running through them in sequence. The techniques further provide for scaling the ONN to different sizes. Additional embodiments may be described and claimed.

Abstract: In one embodiment, a matrix operation may be performed to reorder a plurality of dimensions of an input matrix stored in two-dimensional memory. Data associated with the input matrix may be accessed using one or more strided memory operations, wherein the one or more strided memory operations are configured to access the two-dimensional memory at a plurality of locations that are separated by a particular interval. The data accessed using the one or more strided memory operations may be stored in a result matrix, wherein the data accessed using each strided memory operation is stored in the result matrix in non-transpose form or transpose form.

Abstract: In one embodiment, a matrix operation associated with a plurality of input matrices may be performed. The plurality of input matrices may be partitioned into a plurality of input partitions, wherein the plurality of input matrices is partitioned based on a number of available processing elements. The plurality of input partitions may be distributed among a plurality of processing elements, wherein each input partition is distributed to a particular processing element of the plurality of processing elements. A plurality of partial matrix operations may be performed using the plurality of processing elements, and partial matrix data may be transmitted between the plurality of processing elements while performing the plurality of partial matrix operations. A result of the matrix operation may be determined based on the plurality of partial matrix operations.

Abstract: A system receives and executes a sequence of tensor instructions, for example, instructions for performing a neural network computation. The system may be implemented as a multiprocessor architecture, for example, hardware for performing a neural network computation. A tensor instruction specifies a tensor computation receiving one or more input tensors for determining an output tensor. The system stores a decimal position associated with a plurality of values of a tensor. The system performs the tensor computation of a tensor instruction to determine a plurality of values of the output tensor. The system collects statistics describing the plurality of values of the output tensor and determines a decimal position for the plurality of values based on the collected statistics.

Abstract: A network of matrix processing units (MPUs) is provided on a device, where each MPU is connected to at least one other MPU in the network, and each MPU is to perform matrix multiplication operations. Computer memory stores tensor data and a master control central processing unit (MCC) is provided on the device to receive an instruction from a host device, where the instruction includes one or more tensor operands based on the tensor data. The MCC invokes a set of operations on one or more of the MPUs based on the instruction, where the set of operations includes operations on the tensor operands. A result is generated from the set of operations, the result embodied as a tensor value.

Abstract: In one embodiment, a matrix operation associated with a plurality of input matrices may be performed. The plurality of input matrices may be partitioned into a plurality of input partitions, wherein the plurality of input matrices is partitioned based on a number of available processing elements. The plurality of input partitions may be distributed among a plurality of processing elements, wherein each input partition is distributed to a particular processing element of the plurality of processing elements. A plurality of partial matrix operations may be performed using the plurality of processing elements, and partial matrix data may be transmitted between the plurality of processing elements while performing the plurality of partial matrix operations. A result of the matrix operation may be determined based on the plurality of partial matrix operations.

Abstract: In one embodiment, a matrix operation associated with a plurality of input matrices may be performed. The plurality of input matrices may be partitioned into a plurality of input partitions, wherein the plurality of input matrices is partitioned based on a number of available processing elements. The plurality of input partitions may be distributed among a plurality of processing elements, wherein each input partition is distributed to a particular processing element of the plurality of processing elements. A plurality of partial matrix operations may be performed using the plurality of processing elements, and partial matrix data may be transmitted between the plurality of processing elements while performing the plurality of partial matrix operations. A result of the matrix operation may be determined based on the plurality of partial matrix operations.

Abstract: In one embodiment, a matrix operation may be performed to reorder a plurality of dimensions of an input matrix stored in two-dimensional memory. Data associated with the input matrix may be accessed using one or more strided memory operations, wherein the one or more strided memory operations are configured to access the two-dimensional memory at a plurality of locations that are separated by a particular interval. The data accessed using the one or more strided memory operations may be stored in a result matrix, wherein the data accessed using each strided memory operation is stored in the result matrix in non-transpose form or transpose form.

Abstract: In one embodiment, a matrix operation may be performed using a plurality of input matrices, wherein the matrix operation is associated with one or more convolution operations. The plurality of input matrices may be partitioned into a plurality of input partitions, wherein the plurality of input matrices is partitioned based on a number of available processing elements. The plurality of input partitions may be distributed among a plurality of processing elements, wherein each input partition is distributed to a particular processing element of the plurality of processing elements. A plurality of partial matrix operations may be performed using the plurality of processing elements, and partial matrix data may be transmitted between the plurality of processing elements while performing the plurality of partial matrix operations. A result of the matrix operation may be determined based on the plurality of partial matrix operations.

Abstract: In one embodiment, a matrix operation associated with a plurality of input matrices may be performed. The plurality of input matrices may be partitioned into a plurality of input partitions, wherein the plurality of input matrices is partitioned based on a number of available processing elements. The plurality of input partitions may be distributed among a plurality of processing elements, wherein each input partition is distributed to a particular processing element of the plurality of processing elements. A plurality of partial matrix operations may be performed using the plurality of processing elements, and partial matrix data may be transmitted between the plurality of processing elements while performing the plurality of partial matrix operations. A result of the matrix operation may be determined based on the plurality of partial matrix operations.

Abstract: Described herein are methods, systems, and apparatuses to utilize a matrix operation by accessing each of the operation's matrix operands via a respective single memory handle. This use of a single memory handle for each matrix operand eliminates significant overhead in memory allocation, data tracking, and subroutine complexity present in prior art solutions. The result of the matrix operation can also be accessible via a single memory handle identifying the matrix elements of the result.