Structured-Sparse Tile Matrix FMA for Lower Instruction Intensity
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Solution Overview
Problem
Existing processors face challenges in efficiently handling large matrices due to instruction intensity and difficulty in managing rows of matrices, especially in larger matrix operations.
Innovation Solution
Implementing 2-dimensional data structures called tiles, which are configured and operated on using specialized matrix operations, including tile multiplication, addition, and accumulation, with support for packed data registers and shared memory access, to optimize matrix operations in CPUs, GPUs, and accelerators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processors handle large matrices using standard instructions, then general-purpose computing is maintained, but instruction intensity increases and processing efficiency decreases
Solution Approach 1:
The patent divides large matrices into smaller tile blocks that can be processed independently and in parallel. Each tile is a compact data structure that fits within register files, enabling efficient batch processing of matrix elements without requiring complex sequential instructions for entire large matrices.
Solution Approach 2:
The patent introduces a two-dimensional tile data structure organization instead of traditional one-dimensional vector processing. Tiles arrange matrix elements in 2D grids that map naturally to matrix operation semantics, enabling simultaneous processing of multiple elements through dimension-aligned operations.
2Productivity
If 2D tile data structures are implemented for matrix operations, then parallel processing efficiency improves, but data structure complexity and memory management difficulty increase
Solution Approach 1:
The patent designs tiles as universal data structures that can represent various matrix dimensions and configurations through configurable parameters. A single tile implementation handles different matrix sizes and operation types (multiplication, addition, transposition) through parameterized operations, reducing the need for specialized data structures for each case.
Solution Approach 2:
The patent implements a hierarchical memory organization where tiles are nested within larger matrix structures. Individual tiles contain elements that are themselves organized in rows and columns, allowing multi-level parallelism where operations can be performed on individual elements, entire tiles, or collections of tiles simultaneously.
3Productivity
If specialized matrix operation hardware is added to processors, then compute throughput improves, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent uses replicated tile processing units that can be instantiated multiple times across the processor architecture. Each tile unit is a simplified copy designed for a specific operation type, and multiple copies work in parallel to achieve high throughput without requiring a single complex universal matrix processor.
Solution Approach 2:
The patent implements configurable tile parameters that allow the same hardware structure to adapt to different matrix operation requirements. By changing operational parameters (tile size, data precision, operation type) rather than hardware structure, the system achieves versatility without increasing physical device complexity.
Data Source
AI summary
Systems, methods, and apparatuses relating sparsity based FMA. In some examples, an instance of a single FMA instruction has one or more fields for an opcode, one or more fields to identify a source/destination matrix operand, one or more fields to identify a first plurality of source matrix operands, one or more fields to identify a second plurality of matrix operands, wherein the opcode is to indicate that execution circuitry is to select a proper subset of FP8 data elements from the first plurality of source matrix operands based on sparsity controls from a first matrix operand of the second plurality of matrix operands and perform a FMA.