Photonic In-Memory Co-Processor for Matrix Multiplication
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Solution Overview
Problem
Traditional computing architectures, such as van-Neumann architectures, are insufficient to handle the exponentially growing amount of data and increasing processing requirements in AI applications, leading to a need for low-power, high-speed data processing devices that can operate as co-processors to traditional computing systems.
Innovation Solution
A co-processor utilizing an optical waveguide crossbar array with photonic memory elements that can perform matrix-matrix multiplication in one step by receiving input signals as optical signals and storing data matrix values, allowing for simultaneous multiplication of input and data matrices using different wavelengths, thereby reducing computational time and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional van-Neumann computing architectures are used to process data, then data can be processed using conventional methods, but the processing speed is insufficient to handle exponentially growing data amounts and AI requirements
Solution Approach 1:
The patent replaces traditional electronic computing mechanisms with photonic mechanisms. Optical waveguides transmit light signals instead of electrical signals, and photonic memory elements store data optically. This substitution enables parallel processing of multiple data operations simultaneously through optical paths, achieving Tera-Multiply-Accumulate per second (TMAC/s) speed and resolving the bottleneck in handling exponentially growing data amounts.
Solution Approach 2:
The patent introduces a spatial dimension for data processing by using optical waveguide crossbar arrays. Data is processed across multiple spatial paths simultaneously rather than sequentially, enabling parallel matrix-matrix multiplications. The crossbar array configuration allows simultaneous computation across numerous processing elements, dramatically increasing productivity while maintaining high speed.
2Productivity
If traditional computing architectures process data, then computations can be performed, but power consumption increases continuously with data amount and processing speed requirements
Solution Approach 1:
The patent substitutes electronic signal transmission with optical signal transmission. Photonic memory elements and optical waveguides enable data processing without the continuous power consumption associated with electronic switching and data movement. The photonic architecture performs computations using light manipulation, which consumes significantly less energy than traditional electronic processing, especially for large-scale matrix operations.
Solution Approach 2:
The patent merges storage and computation functions into a unified photonic memory array. By combining the data storage function (in photonic memory elements) with the computation function (in the optical waveguide crossbar array), the system eliminates the need for separate memory access operations that consume energy in traditional von Neumann architectures. This in-memory computing approach reduces overall power consumption while maintaining high processing capacity.
3Productivity
If data is moved through traditional computing systems for processing, then computations can be performed, but data movement creates bottlenecks and increases processing time
Solution Approach 1:
The patent merges data storage and computation into a single integrated photonic memory array. Data resides in photonic memory elements that are directly part of the computational array, eliminating the need to transfer data between separate memory and processing units. This integration eliminates data movement bottlenecks and reduces processing latency while maintaining high computational efficiency.
Solution Approach 2:
The patent performs data loading into photonic memory elements in advance, allowing computations to proceed without repeated data access. By pre-storing data in the photonic memory array before computation begins, the system eliminates runtime data movement operations that would otherwise create bottlenecks and increase processing time.
4Speed
If matrix-matrix multiplication is performed using traditional methods, then computations can be executed, but the process requires multiple sequential steps rather than simultaneous processing
Solution Approach 1:
The patent transitions from sequential computational steps to simultaneous parallel processing by utilizing the spatial dimension of the optical waveguide crossbar array. Multiple matrix operations are performed concurrently across different spatial paths, enabling the entire matrix-matrix multiplication to be completed in a single time step rather than through sequential operations, dramatically reducing computational time.
Solution Approach 2:
The photonic memory array maintains data continuously during the computation process without requiring sequential access. The optical signals can be processed continuously through the waveguide array, enabling uninterrupted computation. This continuous processing capability allows matrix-matrix multiplication to be performed in parallel across all elements simultaneously, eliminating the time loss associated with sequential processing steps.
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
This approach significantly increases computation speed and reduces power consumption by enabling parallelized photonic in-memory computing, addressing the bottleneck in convolutional neural networks and facilitating the implementation of efficient AI systems with reduced latency and data movement.
Implementation Method 1
a plurality of photonic memory elements, arranged at crossing points of an optical waveguide crossbar array
Implementation Method 2
a receiving unit adapted for receiving input signals for the input matrix as optical signals
Data Source
AI summary
A co-processor for performing a matrix multiplication of an input matrix with a data matrix in one step may be provided. The co-processor receives input signals for the input matrix as optical signals. A plurality of photonic memory elements is arranged at crossing points of an optical waveguide crossbar array. The plurality of memory elements is configured to store values of the data matrix. Input signals are connected to input lines of the optical waveguide crossbar array. Output lines of the optical waveguide crossbar array represent a dot-product between a respective column of the optical waveguide crossbar array and the received input signals, and values of elements of the input matrix to be multiplied with the data matrix correspond to light intensities received at input lines of the respective photonic memory elements. Additionally, different wavelengths are used for each column of the input matrix optical signals.