Block-Sparse Matrix Acceleration Through Row-Column Rearrangement
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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 and deep learning applications.
Innovation Solution
A matrix operation accelerator that can efficiently convert matrices with structured sparsity patterns into unstructured sparsity patterns by rearranging rows and columns, allowing it to perform operations with improved performance and reduced resource usage across various sparsity patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a matrix operation accelerator is designed for a specific sparsity pattern, then it achieves high performance for that pattern, but it becomes inefficient when operating on matrices with different sparsity patterns
Solution Approach 1:
The accelerator is divided into multiple processing engines, each specialized for a particular sparsity pattern (e.g., one engine for pattern A, another for pattern B). The system segments the work by routing different sparsity patterns to appropriate engines, allowing high performance for each pattern while maintaining overall versatility through the multi-engine architecture.
Solution Approach 2:
The accelerator implements a universal interface and control logic that can handle multiple sparsity patterns through a single system. The control unit detects the input sparsity pattern and configures the processing engines accordingly, enabling one system to perform multiple functions across different patterns without requiring complete redesign for each pattern.
2Adaptability or versatility
If the accelerator reconfigures to handle different sparsity patterns, then adaptability improves, but device complexity and routing resources increase
Solution Approach 1:
The accelerator employs dynamic reconfiguration capabilities where processing engines can be programmatically adjusted to match different sparsity patterns. The control logic dynamically selects and configures appropriate engines based on the input pattern, allowing adaptability without requiring permanent complex routing for all possible patterns simultaneously.
Solution Approach 2:
A control unit acts as an intermediary between the input matrices and the processing engines. This mediator detects the sparsity pattern, determines the appropriate engine configuration, and routes the data accordingly, simplifying the overall system architecture by centralizing the complexity in a manageable control layer rather than distributing it throughout the entire system.
3Adaptability or versatility
If multiple processing engines are added to support different sparsity patterns, then versatility improves, but area and power consumption increase
Solution Approach 1:
The system uses segmentation to create multiple specialized processing engines that can be selectively activated. Rather than building one large engine that handles all patterns equally, the system segments functionality into smaller, more efficient engines that can be turned on or off based on the input pattern, reducing the active area and power consumption for any given operation.
Solution Approach 2:
Different processing engines are optimized with local quality tailored to specific sparsity patterns. Each engine has the precise structure and resources needed for its designated pattern, avoiding the waste of providing unnecessary resources for other patterns. This localized optimization reduces overall area and power while maintaining versatility through the collection of specialized engines.
Data Source
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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.