Linear Photonic Processor for Low-Heat Matrix Multiplication
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Solution Overview
Problem
Conventional electrical processors face limitations in speed and efficiency due to electrical properties like impedance, leading to delays and heat generation issues, which are not feasible in large-scale processing operations such as matrix-vector multiplication required in machine learning and deep learning algorithms.
Innovation Solution
A photonic processing architecture that uses incoherent light for matrix-vector multiplication, encoding matrix elements directly in attenuators, and employing modulation schemes with coupled amplitude and phase modulation, allowing for dynamic loss scaling independent of matrix size, thus overcoming the limitations of electrical processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional electrical processors are used for matrix-vector multiplication, then general-purpose computing capability is maintained, but processing speed and efficiency deteriorate due to electrical impedance and heat generation
Solution Approach 1:
The patent replaces electrical processing systems with optical processing systems. Specifically, it uses optical modulators to encode matrix elements and vectors into optical signals, performs matrix-vector multiplication optically, and uses optical detectors to convert results back to electrical signals. This substitution eliminates electrical impedance limitations and reduces heat generation associated with conventional electrical processors.
Solution Approach 2:
The patent changes the fundamental operating parameter from electrical signals to optical signals. By encoding data into optical domains and performing computations using optical properties (such as light intensity modulation), the system achieves higher processing speeds and lower heat generation compared to electrical systems operating at comparable scales.
2Productivity
If conventional electrical processors are used for large-scale matrix operations, then computational capability is maintained, but processing efficiency deteriorates due to impedance-related delays
Solution Approach 1:
The patent substitutes electrical signal processing with optical signal processing to eliminate impedance-related delays. Optical signals propagate without the resistive losses and capacitive effects that limit electrical processor speed, enabling faster matrix-vector multiplication operations essential for machine learning workloads.
3Ease of operation
If optical detectors are used to convert optical signals to electrical signals, then signal conversion is achieved, but output sign determination becomes complex requiring additional control circuitry
Solution Approach 1:
The patent segments the optical detection process into distinct functional components: optical detectors convert optical signals to electrical signals, control circuitry determines the sign of outputs based on switch configurations, and switch networks route signals appropriately. This segmentation allows each component to specialize in a specific task, managing overall system complexity through modular organization.
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
This approach enables highly parallel and power-efficient performance of matrix-vector multiplication, reducing latency and heat generation, and allows for larger matrix sizes with improved programming efficiency compared to traditional electronic systems.
Implementation Method 1
A first plurality of optical modulators may be configured to receive an input optical signal, modulate the input optical signal, and output a first optical signal representing an element of a vector
Implementation Method 2
A plurality of optical detectors may be optically coupled to the second plurality of optical modulators and configured to convert the second optical signal into an electrical signal representing the portion of the matrix-vector multiplication
Data Source
AI summary
Systems and methods for performing signed matrix operations using a linear photonic processor are provided. The linear photonic processor is formed as an array of first amplitude modulators and second amplitude modulators, the first amplitude modulators configured to encode elements of a vector into first optical signals and the second amplitude modulators configured to encode a product between the vector elements and matrix elements into second optical signals. An apparatus may be used to implement a signed value of an output of the linear processor. The linear photonic processor may be configured to perform matrix-vector and/or matrix-matrix operations.