Photonic Matrix Processor Using Beam Splitters for Fast AI Computation
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Solution Overview
Problem
Conventional processors, such as CPUs, are inefficient for computationally intensive algorithms like graphics processing, artificial intelligence, and deep learning due to their general-purpose architecture and electrical signal limitations, which cause delays and energy dissipation.
Innovation Solution
A photonic processing system utilizing a photonic processor with interconnected variable beam splitters and optical encoders to perform matrix multiplications on optical signals, enabling highly parallel linear transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional CPUs are used for computationally intensive algorithms, then general-purpose computing is achieved, but processing speed and energy efficiency deteriorate
Solution Approach 1:
The patent replaces electrical signal processing in conventional CPUs with optical signal processing. Optical signals propagate through waveguides and interact via optical modulators and detectors, eliminating the need for electrical transistors and circuits. This substitution enables parallel processing of multiple data streams simultaneously, achieving high-speed matrix multiplications and other computationally intensive operations without the speed and energy limitations of electrical systems.
2Ease of operation
If electrical signals are used in conventional processors, then computation is performed, but signal delays and energy dissipation occur
Solution Approach 1:
The patent substitutes electrical signals with optical signals throughout the processing system. Optical signals experience minimal attenuation and no resistive heating as they propagate through waveguides. The optical modulators and detectors convert optical signals to perform computations without the energy dissipation inherent in electrical transistor switching, thereby eliminating both signal delays and energy loss while maintaining full computation capability.
3Productivity
If specialized processors like GPUs are developed for particular algorithms, then processing efficiency improves, but device complexity increases
Solution Approach 1:
The patent achieves specialized processing efficiency through optical physics rather than complex electronic architectures. The optical system uses waveguide arrays with fixed geometric configurations that inherently perform matrix multiplications and other linear algebra operations through the physical propagation and interference of light. This eliminates the need for complex control logic, memory hierarchies, and transistor circuits required in GPUs, achieving high algorithmic efficiency with simpler overall device architecture.
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 processing system achieves significant speedup in matrix multiplications, completing tasks like GPU operations in hundreds of picoseconds compared to tens of nanoseconds, overcoming electrical signal delays and energy dissipation.
Implementation Method 1
a plurality of optical encoders (1-1211) configured to encode a plurality of input vectors into the first plurality of optical signals
Implementation Method 2
a first array of interconnected variable beam splitters (VBSs) comprising a first plurality of optical inputs and a first plurality of optical outputs
Implementation Method 3
a plurality of optical detectors (1-1223) configured to detect the second plurality of optical signals
Data Source
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AI summary
Aspects relate to a photonic processing system, a photonic processor, and a method of performing matrix-vector multiplication. An optical encoder may encode an input vector into a first plurality of optical signals. A photonic processor may receive the first plurality of optical signals; perform a plurality of operations on the first plurality of optical signals, the plurality of operations implementing a matrix multiplication of the input vector by a matrix; and output a second plurality of optical signals representing an output vector. An optical receiver may detect the second plurality of optical signals and output an electrical digital representation of the output vector.