Lightmatter, Inc.

Tyler J Kenney

Darius Bunandar

Tomo Lazovich

Martin B Z Forsythe

Nicholas Christopher Harris

Bradley D Dobbie

Ayon Basumallik

Martin Forsythe

Nicholas Moore

Gongyu Wang

Systems and methods for performing matrix operations using a photonic processor are provided. The photonic processor includes encoders configured to encode a numerical value into an optical signal and optical multiplication devices configured to output an electrical signal proportional to a product of one or more encoded values. The optical multiplication devices include a first input waveguide, a second input waveguide, a coupler circuit coupled to the first input waveguide and the second input waveguide, a first detector and a second detector coupled to the coupler circuit, and a circuit coupled to the first detector and second detector and configured to output a current that is proportional to a product of a first input value and a second input value.

Matrix multiplication using optical processing

Techniques for computing matrix operations for arbitrarily large matrices on a finite-sized hybrid analog-digital matrix processor are described. Techniques for gain adjustment in a finite-sized hybrid analog-digital matrix processor are described which enable the system to obtain higher energy efficiencies, greater physical density and improved numerical accuracy. In some embodiments, these techniques enable maximization of the predictive accuracy of a GEMM-based convolutional neural network using low-precision data representations.

Hybrid analog-digital matrix processors

Aspects of the present application relate to techniques for computing convolutions and cross-correlations of input matrices. A first technique is based on the transformation of convolution operations into a matrix-vector product. A second technique is based on two-dimensional matrix multiplication. A third technique is based on the convolution theorem, which states that convolutions correspond to multiplications in a transform space. Embodiments include methods for computing convolutions of a filter matrix and an input data matrix; apparatuses for computing convolutions of a filter matrix and an input data matrix; and a non-transitory computer readable medium programmed with instructions that, when executed by a processor perform a method for computing convolutions of a filter matrix and an input data matrix.

Convolutional layers for neural networks using programmable nanophotonics

Aspects of the present disclosure provide an aligned storage strategy for stripes within a long vector for a vector processor, such that the extra computation needed to track strides between input stripes and output stripes may be eliminated. As a result, the stripe locations are located in a more predictable memory access pattern such that memory access bandwidth may be improved and the tendency for memory error may be reduced.

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Boston, MA, United States of America

Tyler J Kenney