Neural Network Sparse Weight Conversion for Higher GPU Sparsity
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Solution Overview
Problem
Existing GPU implementations lack dedicated kernel support for higher sparsity levels beyond 50% in neural networks, leading to inefficiencies in computation and memory usage, particularly in large language models, which require significant computational resources and increase capital expenditure and inference time.
Innovation Solution
A system and method that converts weight matrices to higher sparsity ratios using structured sparse instructions, such as 2:4 or 4:8 sparsity, by padding with zero elements and using index bits to indicate non-sparse positions, allowing existing GPU instructions to handle higher sparsity levels efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher sparsity levels (beyond 50%) are used in neural networks, then computation efficiency and memory usage are improved, but existing GPU instructions cannot efficiently process this data without dedicated kernel support
Solution Approach 1:
The patent introduces structured sparse instructions as an intermediary layer between the high-sparsity data format and the GPU execution engine. These specialized instructions act as mediators that translate the compressed sparse data into operations the GPU can efficiently execute, enabling higher sparsity levels without requiring fundamental changes to the GPU architecture.
Solution Approach 2:
The patent changes the sparsity parameter from a fixed 50% limitation to variable sparsity levels (e.g., 25%, 50%, 75%). By allowing the sparsity ratio to be configurable and adapting the instruction format accordingly, the system achieves improved computation efficiency for higher sparsity while managing complexity through parameterization rather than architectural redesign.
2Loss of energy
If structured sparse instructions with padding are used, then data movement from memory is reduced, but additional processing steps are required for conversion and padding operations
Solution Approach 1:
The patent performs padding and structuring operations in advance during data loading or preprocessing stages. By preparing the sparse data in the required format before it reaches the computation kernel, the system reduces the burden on the execution engine and minimizes additional processing complexity during the actual neural network computation.
Solution Approach 2:
The patent extracts and removes unnecessary data elements (padding zeros) from the data stream before computation. By taking out the redundant padding information and only transmitting the essential non-zero elements along with their position metadata, the system significantly reduces memory bandwidth consumption while the extraction operation itself is performed efficiently through dedicated hardware support.
3Quantity of substance
If quantization is applied to reduce data precision, then storage space and data movement are reduced, but computational precision may be compromised
Solution Approach 1:
The patent changes the precision parameter dynamically based on the computational requirements and sparsity level. By adjusting the quantization precision parameter (e.g., using INT8 for sparse weights with 50% sparsity, or higher precision for lower sparsity levels), the system achieves reduced storage size and data movement while maintaining sufficient computational precision through adaptive parameter selection.
Data Source
AI summary
A system and method for processing, executing or training a neural network (NN) may input a first weight matrix storing weights using sparsity defined by a first ratio of non-sparse elements to elements; convert the first weight matrix to a second weight matrix storing weights using sparsity defined by a second ratio of non-sparse elements to elements; and input the second weight matrix into a processor instruction designed to use an input defined by a second ratio of non-sparse elements to elements.