Photonic Tensor Accelerators for Scalable Low-Power ANN Multiplication
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Solution Overview
Problem
Electronic hardware accelerators for artificial neural networks (ANNs) have reached scalability limits due to power density issues, and existing optical computing methods face challenges in scalability, power consumption, and compatibility with ANN operations.
Innovation Solution
A photonic unit for vector-vector, matrix-vector, matrix-matrix, batch matrix-matrix, and tensor-tensor multiplication using optical multiplexers, beam combiners, and duplicators that encode and combine optical signals across multiple dimensions of light to perform multiplications efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electronic hardware accelerators are used for ANN operations, then computing power is improved, but power density and heat dissipation become limiting factors
Solution Approach 1:
The patent replaces electronic computing systems with photonic computing systems. Optical multiplexers, beam combiners, and photodetectors are used to perform matrix multiplications and other ANN operations using light instead of electricity, thereby avoiding the power density and heat dissipation limitations of electronic hardware accelerators.
Solution Approach 2:
The patent utilizes multiple dimensions of optical signals (wavelength, spatial mode, polarization, time) to encode and process information simultaneously. This multi-dimensional approach enables parallel processing of multiple data streams through a single photonic pathway, dramatically increasing computing power without proportionally increasing power consumption.
2Use of energy by stationary object
If optical computing methods are used, then power consumption is reduced, but scalability and compatibility with ANN operations face challenges
Solution Approach 1:
The photonic unit is designed to perform multiple ANN operations including matrix-vector multiplication, matrix-matrix multiplication, batch matrix-matrix multiplication, and tensor operations. The same core components (optical multiplexers, beam combiners, photodetectors) are used across different operation types, making the system versatile and compatible with various ANN architectures.
Solution Approach 2:
The patent divides complex ANN operations into discrete optical processing stages. Each stage handles specific computational tasks using dedicated optical components, allowing the system to scale by adding or removing stages rather than redesigning the entire system. This modular segmentation improves both scalability and adaptability.
3Productivity
If electronic accelerators are scaled up, then processing capability increases, but heat dissipation and power density become problematic
Solution Approach 1:
The patent replaces electronic processing components that generate significant heat with photonic components that operate with minimal heat generation. Optical signals carry information without the resistive heating inherent in electronic systems, enabling high processing capability without proportional heat dissipation issues.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The photonic unit achieves several orders of magnitude higher scalability and lower power consumption than electronic accelerators, enabling efficient matrix and tensor multiplications suitable for ANN operations.
Implementation Method 1
a beam combiner that receives the first multiplexed optical signal from the first optical multiplexer and second multiplexed optical signal from the second optical multiplexer so as to combine them to produce an interference between the first optical signal and second optical signal containing multiplication results of the first vector and the second vector in a total interference intensity
Data Source
AI summary
Photonic units for vector-vector multiplication, matrix-vector multiplication, matrix-matrix multiplication, batch matrix-matrix multiplication, and tensor-tensor multiplication are described. Multiplications are through coherent mixing and square-law detection. There are many dimensions—wavelength, vector mode, quadrature, and three dimensions of space—that can be used to construct photonic accelerators. The encoded input vector or input matrix is fanned out into a desired number of copies and mixed with the corresponding encoded local oscillators containing the weight vectors comprising the weight matrix. Any subset of two (three) dimensions can be used to construct photonic accelerators for matrix-vector (matrix-matrix) multiplications. Multiple dimensions can be combined into a hyperdimension to increase the scalability. Each dimension, each non-overlapping subset of a dimension, or each non-overlapping subset of a hyperdimension, can be used independently to construct a photonic tensor accelerator (PTA) for batch matrix multiplication, or tensor multiplication operations.