Machine Learning Matrix Accelerator for Block Sparsity Conversion
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Solution Overview
Problem
Existing matrix operation accelerators designed for one sparsity pattern are inefficient when operating on matrices with different sparsity patterns, leading to increased resource consumption and latency in machine learning applications.
Innovation Solution
A matrix operation accelerator is designed to operate efficiently on matrices with unstructured sparsity by rearranging the rows and columns to convert the sparsity pattern to a more suitable format for the accelerator, optimizing performance across various sparsity patterns without requiring separate hardware configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a matrix operation accelerator is designed for a specific sparsity pattern, then it achieves high efficiency on that pattern, but it becomes inefficient when operating on matrices with different sparsity patterns
Solution Approach 1:
The system dynamically reconfigures the accelerator architecture to match the input matrix sparsity pattern. Control logic detects the sparsity pattern type and activates corresponding processing units or data layouts, allowing the same hardware to adapt its behavior for different sparsity patterns (e.g., unstructured, block, or diagonal sparsity) without physical reconfiguration
Solution Approach 2:
The patent changes operational parameters such as data layout configuration, processing unit activation, and memory access patterns based on the detected sparsity pattern. By adjusting these parameters dynamically, the accelerator maintains high efficiency across different matrix types without requiring separate hardware designs for each pattern
2Productivity
If separate hardware configurations are created for different sparsity patterns, then efficiency on each pattern improves, but device complexity and hardware costs increase
Solution Approach 1:
The accelerator is designed as a universal platform that can handle multiple sparsity patterns through software-controlled reconfiguration rather than dedicated hardware for each pattern. A single accelerator instance performs multiple functions by changing its operational mode based on the input matrix characteristics, eliminating the need for separate hardware configurations
Solution Approach 2:
Control logic acts as an intermediary between the input matrix and the processing units. This control layer detects the sparsity pattern and mediates the data flow, routing operations appropriately without requiring the underlying hardware architecture to be physically reconfigured for each pattern type
3Device complexity
If the accelerator processes matrices with unstructured sparsity directly, then it maintains simple architecture, but resource consumption and latency increase
Solution Approach 1:
The system performs preliminary detection of the sparsity pattern before processing the matrix operation. Based on this detection, it pre-configures the data layout and activates the appropriate processing units in advance, avoiding latency during the actual computation phase
Data Source
AI summary
This disclosure relates matrix operation acceleration for different matrix sparsity patterns. A matrix operation accelerator may be designed to perform matrix operations more efficiently for a first matrix sparsity pattern rather than for a second matrix sparsity pattern. A matrix with the second sparsity pattern may be converted to a matrix with the first sparsity pattern and provided to the matrix operation accelerator. By rearranging the rows and/or columns of the matrix, the sparsity pattern of the matrix may be converted to a sparsity pattern that is suitable for computation with the matrix operation accelerator.