3D Integrated Optical Neural Network Accelerator Panels
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Solution Overview
Problem
Existing optical neural network (ONN) accelerators face sub-optimal energy efficiency, compute density, and scalability due to high energy consumption, low precision, and complex designs, primarily limited by electro-optic conversion efficiency, channel crosstalk, and phase instability in existing silicon integrated photonics-based systems.
Innovation Solution
A 3D integrated discrete photonics ONN accelerator with a highly scalable surface-emitting semiconductor laser array and voltage-controlled semiconductor saturable absorber-based nonlinear elements, divided into input, weight, and photodetector panels, performing matrix-matrix multiplications in parallel with inherent inline optical nonlinearity, enhancing energy efficiency and compute density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If silicon integrated photonics-based systems are used, then optical computation capability is achieved, but energy consumption increases due to low electro-optic conversion efficiency
Solution Approach 1:
The patent replaces electro-optic conversion mechanisms with direct optical computation. Optical signals are processed through diffractive optical elements and optical modulators without requiring conversion to electrical domain, eliminating the energy loss associated with electro-optic conversion while maintaining optical computation capability
Solution Approach 2:
The patent extracts and removes the electro-optic conversion stage from the computational system. By implementing pure optical processing using diffractive elements and optical modulators, the system eliminates the intermediate electrical conversion step that causes energy loss, achieving direct optical-to-optical computation
2Productivity
If existing optical neural network accelerators are deployed, then computation speed is improved, but compute density decreases due to large component footprints
Solution Approach 1:
The patent implements a nested architecture where multiple computational layers are vertically stacked and integrated within a compact three-dimensional structure. The diffractive optical elements and optical modulators are arranged in nested configurations that maximize computational density while minimizing the horizontal footprint of the system
Solution Approach 2:
The patent transitions from two-dimensional planar layouts to three-dimensional vertical integration. By stacking computational layers and using vertical optical paths with diffractive elements, the system achieves high compute density without increasing the lateral component footprint, effectively utilizing the vertical dimension for scaling
3Productivity
If existing optical neural network accelerators are used, then processing capability is enhanced, but design complexity increases due to channel crosstalk and phase instability
Solution Approach 1:
The patent introduces diffractive optical elements as intermediary components that mediate between optical signals and computational operations. These elements provide inherent optical nonlinearity and signal conditioning, reducing the need for additional control mechanisms and simplifying the overall system design while maintaining processing capability
Solution Approach 2:
The patent implements self-aligning and self-correcting optical paths using diffractive optical elements. The inherent optical nonlinearity and diffraction properties automatically compensate for channel crosstalk and phase instability without requiring external control systems, reducing design complexity while enhancing processing capability
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 achieves high-speed, high-energy efficiency, and high compute density while maintaining accuracy, enabling faster processing speeds and increased scalability for applications like speech recognition and image processing.
Implementation Method 1
a highly scalable surface-emitting semiconductor laser array
Implementation Method 2
voltage-controlled semiconductor saturable absorber-based nonlinear elements
Implementation Method 3
the photodetector panel is to generate output photodetector signals based on the output optical signals
Data Source
AI summary
Systems, apparatuses and methods include technology that executes, with a first plurality of panels, a first matrix-matrix multiplication operation of a first layer of an optical neural network (ONN) to generate output optical signals based on input optical signals that pass through an optical path of the ONN, and weights of the first layer of the ONN. The first plurality of panels includes an input panel, a weight panel and a photodetector panel. The executing includes generating, with the input panel, the input optical signals, where the input optical signals represent an input to the first matrix-matrix multiplication operation of the first layer of the ONN, representing, with the weight panel, the weights of the first layer of the ONN, and generating, with the photodetector panel, output photodetector signals based on the output optical signals that are generated based on the input optical signals and the weights.