Photonic Matrix Accelerator Residue Number System
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Solution Overview
Problem
Conventional electronic computer processors face power and speed limitations due to power dissipation, and photonic processors present design considerations such as limited dynamic range and precision in calculations.
Innovation Solution
A photonic linear processor using a residue number system (RNS) performs calculations by shifting the phase of light signals with phase shifters and detecting the shifted phase with a coherent receiver, enabling efficient summation and error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional electronic processors are used, then calculations can be performed, but power dissipation limits speed and efficiency
Solution Approach 1:
The patent replaces electronic signal processing with photonic signal processing. Light signals propagate through optical waveguides and interact with phase modulators to perform calculations, eliminating the need for electron-based logic gates and transistors that generate heat. This substitution of electronic systems with photonic systems directly addresses the power dissipation bottleneck while maintaining high-speed computation capabilities.
Solution Approach 2:
The patent changes the fundamental operating parameter from electrical current to optical phase. By encoding data in the phase of light waves and performing arithmetic operations through phase modulation and interference, the system achieves computation without the resistive heating that limits electronic processors. This parameter change enables sustained high-speed operation without power dissipation constraints.
2Measurement precision
If photonic processors use residue number system for calculations, then precision is improved, but system complexity increases
Solution Approach 1:
The patent segments large numerical calculations into multiple residue number representations. By decomposing numbers into residues modulo different primes and performing parallel photonic calculations on each residue, the system achieves high precision through composition of simpler modular operations. This segmentation allows precision improvement without proportionally increasing overall system complexity, as each modular channel can be implemented with identical photonic circuitry.
Solution Approach 2:
The patent implements a universal photonic circuit design that can perform multiple arithmetic operations (addition, multiplication, etc.) using the same hardware infrastructure. The residue number system framework allows a single photonic processor configuration to handle diverse computational tasks by changing only the input residue values, not the underlying optical circuit architecture. This multi-functionality reduces the complexity penalty that would otherwise result from precision-enhancing features.
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 linear processor achieves efficient and precise calculations, overcoming power and speed limitations of conventional electronic processors, and provides error correction capabilities.
Implementation Method 1
at least one phase shifter configured to shift, by each of the first value and the second value, a phase of a first light signal
Implementation Method 2
at least one coherent receiver configured to detect the shifted phase of the first light signal
Data Source
AI summary
A photonic processor uses light signals and a residue number system (RNS) to perform calculations. The processor sums two or more values by shifting the phase of a light signal with phase shifters and reading out the summed phase with a coherent detector. Because phase winds back every 2π radians, the photonic processor performs addition modulo 2π. A photonic processor may use the summation of phases to perform dot products and correct erroneous residues. A photonic processor may use the RNS in combination with a positional number system (PNS) to extend the numerical range of the photonic processor, which may be used to accelerate homomorphic encryption (HE)-based deep learning.