Photonics-Based Processor Angular Representation Training
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Solution Overview
Problem
Conventional processors, such as CPUs, are not optimized for the specific computational patterns of deep learning and matrix-based differentiable programs, leading to inefficiencies and long processing times in tasks like natural language processing and object recognition.
Innovation Solution
A photonics-based processor represents matrix values in an angular representation, using singular value decomposition to decompose matrices into unitary and diagonal components, allowing for parallel computation of gradients and efficient training of matrix-based differentiable programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional CPUs are used for deep learning computation, then general purpose computing is achieved, but processing speed is slow due to lack of optimization for specific computational patterns
Solution Approach 1:
The patent replaces conventional electrical computing systems with photonic computing systems. Light-based computation substitutes electron-based processing, enabling parallel matrix operations and gradient computations required for deep learning training, thereby achieving both speed improvement and adaptability to specific computational patterns
Solution Approach 2:
The patent transforms the computational approach by changing from sequential electrical signal processing to parallel photonic signal processing. By utilizing the wave nature of light and implementing matrix operations through optical interference and diffraction, the system achieves fundamental parameter changes in computation speed and parallelism
2Speed
If specialized hardware architectures are developed to speed up deep learning, then processing speed is improved, but device complexity increases
Solution Approach 1:
The photonic computing system is designed to perform multiple functions including matrix multiplication, gradient computation, and parameter updates within a single integrated platform. The system can handle different layers and operations of neural networks using the same photonic hardware, reducing overall system complexity while maintaining high processing speed
Solution Approach 2:
The patent transitions from traditional planar circuit layouts to three-dimensional photonic structures utilizing light propagation in multiple dimensions. This enables simultaneous execution of multiple computational operations through spatial multiplexing, achieving high speed processing without proportionally increasing device complexity
3Reliability
If training stage computation is performed on conventional processors, then model training is achieved, but training time is excessively long due to intensive computation requirements
Solution Approach 1:
The patent replaces sequential electrical computation with parallel photonic computation for training operations. Optical interference patterns enable simultaneous calculation of multiple gradient values, reducing training time while maintaining the reliability and accuracy of model training through precise optical measurement and control
Data Source
AI summary
Methods and apparatus for training a matrix-based differentiable program using a photonics-based processor. The matrix-based differentiable program includes at least one matrix-valued variable associated with a matrix of values in a Euclidean vector space. The method comprises configuring components of the photonics-based processor to represent the matrix of values as an angular representation, processing, using the components of the photonics-based processor, training data to compute an error vector, determining in parallel, at least some gradients of parameters of the angular representation, wherein the determining is based on the error vector and a current input training vector, and updating the matrix of values by updating the angular representation based on the determined gradients.