Matrix Data Scatter Gather Between Rows and Irregular Memory
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Solution Overview
Problem
Current mainstream processors face difficulties in efficiently handling large matrices, requiring numerous instruction-intensive operations such as loading, processing, and storing matrix data, which becomes impractical for larger matrices due to the complexity and resource intensity of these operations.
Innovation Solution
The implementation of a matrix operations accelerator that utilizes 2-dimensional data structures called 'tiles' to perform operations like multiplication, addition, and storage, allowing for efficient configuration and processing of matrix data through specialized instructions and circuitry, including tile configuration, load/store operations, and fused multiply accumulate instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processor operations are used for large matrices, then processing capability is maintained, but operation complexity and resource intensity increase significantly
Solution Approach 1:
The patent divides large matrices into smaller 2D tile blocks that can be processed independently. Each tile is a compact sub-matrix that fits in register file, allowing the processor to break down complex large-matrix operations into simpler, manageable tile-level operations while maintaining overall processing capability
Solution Approach 2:
The patent introduces a 2D tile structure as an intermediate representation between 1D vector operations and full matrix operations. This 2D dimensionality allows for more efficient data organization and processing patterns, enabling parallel operations across both rows and columns simultaneously
2Reliability
If more instructions are used to handle large matrices, then processing completeness is improved, but energy consumption increases
Solution Approach 1:
The patent combines multiple operations (load, store, multiply, accumulate) into fused tile operations that execute in fewer instruction cycles. By merging these operations at the tile level, the processor reduces the total number of instructions needed while ensuring complete processing of all matrix elements
Solution Approach 2:
The patent enables continuous processing of tile blocks through pipelined execution and efficient register reuse. Once tiles are loaded into the register file, all subsequent operations (multiplication, accumulation, storage) occur continuously without repeated memory access, minimizing idle cycles and energy consumption
3Adaptability or versatility
If data is stored in irregularly spaced memory locations, then data organization flexibility is improved, but access efficiency deteriorates
Solution Approach 1:
The patent introduces the 2D tile structure as an intermediary buffer between irregularly spaced memory locations and the processing units. Tiles are loaded from irregular memory locations into the regular, contiguous register file structure, where processing occurs efficiently. This intermediary layer decouples the irregular memory organization from the processing requirements
Solution Approach 2:
The patent applies different data organization strategies at different levels: irregular spacing is maintained in main memory for flexibility, while 2D tiles in the register file provide regular, contiguous storage for efficient access. Each level has optimized characteristics suited to its specific function
Data Source
AI summary
Embodiments for gathering and scattering matrix data by row are disclosed. In an embodiment, a processor includes a storage matrix, a decoder, and execution circuitry. The decoder is to decode an instruction having a format including an opcode field to specify an opcode and a first operand field to specify a set of irregularly spaced memory locations. The execution circuitry is to, in response to the decoded instruction, calculate a set of addresses corresponding to the set of irregularly spaced memory locations and transfer a set of rows of data between the storage and the set of irregularly spaced memory locations.