Quantized Tensor Matrix Multiplication for Low-Bandwidth Compute
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Solution Overview
Problem
Existing computing environments face limitations in performing tensor computations due to resource-intensive operations on general-purpose processors and the need for hardware-specific programming, which increases complexity and verification costs.
Innovation Solution
A computer program product and method that performs tensor matrix multiplication with quantization, allowing for seamless format conversion within an instruction execution, supporting different data types and reducing the need for external data conversions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tensor computations are performed using many instructions on a general-purpose processor, then flexibility and adaptability are maintained, but processing speed and computational efficiency deteriorate
Solution Approach 1:
The patent segments the tensor computation process into distinct phases: data loading from memory, quantization conversion to compact format, matrix multiplication execution, and result generation. This segmentation allows optimization of each phase independently, particularly enabling efficient use of specialized hardware accelerators for the computationally intensive matrix multiplication while maintaining general-purpose processor control for flexibility.
Solution Approach 2:
The patent introduces an intermediary quantization mechanism that converts tensor data between different precision formats (e.g., float32 to int8). This intermediary conversion layer enables efficient hardware acceleration by transforming data into formats optimized for specific accelerator architectures while maintaining compatibility with general-purpose processors and preserving computational accuracy.
2Productivity
If purpose-built hardware implementation is used for tensor computations, then processing efficiency is improved, but programming complexity and verification costs increase due to hardware-specific compilation requirements
Solution Approach 1:
The patent creates a universal computation framework where a single high-level tensor operation can be executed across multiple hardware platforms (general-purpose processors, specialized accelerators, GPUs). The quantization mechanism and standardized instruction interface enable the same computational logic to be deployed on diverse hardware without requiring hardware-specific programming or recompilation, thus reducing development complexity while maintaining hardware optimization benefits.
3Measurement precision
If high-precision data formats are used for tensor elements, then computational accuracy is maintained, but memory pressure and data bandwidth requirements increase
Solution Approach 1:
The patent dynamically changes the precision parameter of tensor data during computation. Input tensors can be stored in high-precision formats (e.g., float32) for accuracy, but are converted to compact formats (e.g., int8, int4) during the quantization phase before matrix multiplication. This parameter change reduces memory bandwidth requirements and storage requirements by up to 4x while maintaining computational accuracy through careful quantization strategy and scaling factor management.
Data Source
AI summary
Tensor multiplication with quantization includes obtaining first and second input tensors, obtaining elements of a selected data type based on elements of the first input tensor and elements of the second input tensor, performing matrix multiplication on the elements of the selected data type, the matrix multiplication including performing quantization of intermediate results, and the quantization scaling the intermediate results to provide scaled results of the matrix multiplication, and generating output elements, for an output tensor, using the scaled results. Optional additional quantization is performed on elements of an input tensor to provide at least some of the elements of the selected data type for the matrix multiplication.