Structured-Sparse Tile FMA for High-Throughput Matrix Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing processors face challenges in efficiently handling large matrices due to the complexity and instruction intensity of matrix operations, particularly in tasks such as matrix multiplication, which is computationally demanding and inefficient for larger matrices.
Innovation Solution
The implementation of 2-dimensional data structures called tiles, which are configured and operated on using specialized hardware and instructions, such as fused multiply accumulate (FMA) circuits, to facilitate efficient matrix operations in CPUs, GPUs, and accelerators, supporting operations like matrix multiplication and accumulation, with support for various data types and sparsity handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processors are used for matrix operations, then general-purpose computing is maintained, but computational efficiency and throughput for large matrices deteriorates
Solution Approach 1:
The patent divides large matrices into smaller tile blocks that can be processed independently and in parallel. This segmentation allows the processor to handle large matrix operations by breaking them down into manageable units, improving computational efficiency while maintaining reasonable device complexity through modular processing
Solution Approach 2:
The patent introduces a tile-based dimensional organization for matrix data, transforming traditional 2D matrix operations into multi-dimensional tile processing. This adds spatial organization dimensions to the computation, enabling more efficient memory access patterns and parallel processing without significantly increasing overall system complexity
2Power
If specialized hardware for matrix multiplication is implemented, then peak compute and energy efficiency improve, but device complexity increases
Solution Approach 1:
The patent designs a tile processing architecture that can perform multiple matrix operations (multiplication, accumulation, transpose, etc.) using the same hardware resources. This multi-functionality allows specialized hardware to achieve high peak compute for matrix operations while avoiding the complexity of completely separate dedicated circuits for each operation type
Solution Approach 2:
The patent combines matrix multiplication and accumulation operations into a single Fused Multiply-Accumulate (FMA) tile instruction. This merging of operations reduces the number of separate hardware components needed, improving peak compute efficiency while controlling hardware complexity through operation fusion
3Productivity
If 8-bit floating-point precision is used, then throughput and energy efficiency improve, but numerical accuracy may deteriorate
Solution Approach 1:
The patent changes the precision parameter from traditional 32-bit or 64-bit floating-point to 8-bit floating-point format for matrix operations. This parameter change significantly improves throughput and energy efficiency by reducing data size and computational complexity, while the tile-based architecture and FMA operations help maintain acceptable numerical accuracy through accumulated precision in intermediate results
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.