Photonic Tensor Manipulation for Deep Learning Speed
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep learning algorithms require long processing times due to conventional computer processors being optimized for general-purpose computing rather than the specific patterns of data movement and computation needed for matrix-based differentiable programs.
Innovation Solution
A system utilizing optical switches and a controller to obtain nominal and transpose orientation vectors of input vectors, leveraging photonic technology for efficient tensor data manipulation, allowing for minimal latency and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional computer processors are used for deep learning computation, then general-purpose computing capability is maintained, but processing time becomes excessively long
Solution Approach 1:
The patent replaces conventional electronic processors with photonic processors that use light instead of electricity for computation. This substitution enables parallel processing of matrix operations through optical interference patterns, dramatically increasing processing speed for deep learning workloads while maintaining computational functionality.
Solution Approach 2:
The patent introduces a new dimensional approach to computing by using spatial light modulation and optical field manipulation in three-dimensional space. This allows simultaneous execution of multiple computational operations through different spatial paths and wavelengths, effectively adding computational dimensions that conventional two-dimensional circuit boards cannot provide.
2Use of energy by moving object
If conventional processors are used for matrix-based differentiable programs, then computational flexibility is maintained, but energy consumption increases
Solution Approach 1:
The patent substitutes electronic signal processing with photonic signal processing, eliminating resistive heating and dynamic power consumption associated with electronic transistors. Optical signals propagate without resistance and can be switched using low-energy photonic devices, dramatically reducing energy consumption while preserving computational versatility through programmable optical circuits.
Solution Approach 2:
The patent uses optical copying and interference techniques where information is replicated across multiple optical paths simultaneously. This allows the same computational operation to be performed in parallel across multiple data elements, reducing the total energy required per computation by distributing the workload across optical copies rather than sequential electronic processing.
3Productivity
If specialized hardware architectures are developed for deep learning, then processing speed increases, but device complexity and scalability become challenges
Solution Approach 1:
The patent creates a universal photonic processing platform that can execute multiple deep learning operations through reconfigurable optical circuits. The same photonic processor can perform different matrix multiplications, convolutions, and tensor operations by programmatically adjusting optical phase shifters and modulators, eliminating the need for separate specialized hardware for each operation type.
Solution Approach 2:
The patent implements dynamically reconfigurable optical circuits where the computational graph and data flow can be changed in real-time through electronic control of optical components. This dynamic reconfigurability allows the hardware to adapt to different algorithms and model architectures without physical redesign, enabling high productivity across diverse deep learning workloads while maintaining manageable complexity.
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 solution enables fast and energy-efficient execution of tensor operations, scalable for vast data handling and optimized for machine learning applications.
Implementation Method 1
The optical switches comprise at least one of a Mach-Zehnder interferometer
Implementation Method 2
The optical switches comprise at least one of a Mach-Zehnder interferometer, a microring resonator
Implementation Method 3
The optical receiver comprises a photodetector
Data Source
AI summary
A method for manipulating an input vector is described. The method involves controlling a plurality of optical switches to obtain a nominal orientation vector or a transpose orientation vector based on a plurality of input optical signals encoding the input vector and received at the plurality of optical switches. The nominal orientation vector and the transpose orientation vector represent transposed versions of one another. A memory system comprising a first section configured to store vectors in accordance with a nominal orientation and a second section configured to store vectors in accordance with a transpose orientation. A controller stores the nominal orientation vector in the first section of the memory system or stores the transpose orientation vector in the second section of the memory system.