Matrix Transpose and Mask Vector Summation for High-Bit-Width AI Processing
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Solution Overview
Problem
Existing hardware solutions for artificial intelligence computations, such as neural networks, face challenges in being flexible and computationally efficient while handling matrix operations like summations, multiplications, and dot products without increasing hardware complexity and expense.
Innovation Solution
A device comprising a matrix transpose component, a matrix processing component, a data alignment component, and a data reduction component, which allows for efficient processing of high-bit-width numbers using a low-bit-width matrix processing component, enabling the summation of high-bit-width numbers by transposing input matrices and performing matrix multiplications with mask vectors, thereby conserving hardware resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-bit-width matrix processing is implemented using native high-bit-width hardware, then processing precision is improved, but device complexity and hardware cost increase
Solution Approach 1:
The patent segments high-bit-width numbers into multiple low-bit-width segments. For example, a 32-bit number is divided into four 8-bit segments. This allows the use of simpler low-bit-width processing components while maintaining the ability to handle high-bit-width data through segmentation and recombination operations.
Solution Approach 2:
The patent introduces intermediary components including a transpose component that reorganizes segmented data, a low-bit-width processing component that performs computations on segments, and a data alignment component that reassembles results. These intermediaries enable high-bit-width processing through low-bit-width hardware without direct high-bit-width computation units.
2Adaptability or versatility
If multiple hardware designs are created to handle different data formats, then adaptability is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent creates a universal processing architecture that can handle multiple data formats and bit-widths using the same hardware components. The system processes different bit-widths (e.g., 8-bit, 16-bit, 32-bit) by uniformly applying segmentation, transposition, and reassembly operations, eliminating the need for format-specific hardware designs.
Solution Approach 2:
The patent changes the parameter of data representation by transforming high-bit-width numbers into segmented low-bit-width representations. This parameter transformation allows a single hardware design to adapt to different original bit-width requirements through configurable segmentation strategies rather than requiring separate hardware for each format.
3Measurement precision
If high-bit-width numbers are processed directly, then measurement precision is maintained, but processing speed and throughput decrease
Solution Approach 1:
By segmenting high-bit-width numbers into smaller units, the patent enables parallel processing of multiple segments simultaneously through the low-bit-width processing component. This segmentation strategy maintains the precision of the original high-bit-width data while increasing throughput by processing segments in parallel rather than as monolithic high-bit-width values.
Data Source
AI summary
A device includes a matrix transpose component, a matrix processing component, a data alignment component, and a data reduction component. The matrix transpose component is configured to transpose an input matrix of elements to output an output matrix of the elements that have been transposed. The matrix processing component is configured to multiply a first multiplication input matrix with a second multiplication input matrix, wherein the output matrix of the matrix transpose component is utilized as the first multiplication input matrix and a mask vector is utilized as the second multiplication input matrix. The data alignment component is configured to modify at least a portion of elements of a result of the matrix processing component. The data reduction component is configured to sum at least the elements of the modified result of the matrix processing component to determine a sum of the group of values.