Matrix Tile Processing Hardware for Efficient Data Operations
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Solution Overview
Problem
Existing mainstream processors face difficulties and inefficiencies in handling large matrices due to the need for extensive instruction sets and complex operations.
Innovation Solution
The implementation of matrix operations in computer hardware using 2-D data structures referred to as 'tiles,' which allows for efficient processing of matrices through operations such as multiplication, addition, and transpose, utilizing specialized circuitry and instruction sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional instruction sets are used for matrix operations, then processors can handle general computing tasks, but processing efficiency for large matrices deteriorates due to extensive instruction requirements
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 matrix operations in manageable chunks, improving efficiency without requiring complex instruction sets for entire large matrices at once.
Solution Approach 2:
The patent introduces a new dimension of operation by implementing dedicated matrix operation instructions that operate on 2-D tile structures rather than traditional 1-D vector operations. This dimensional change enables more efficient memory access patterns and computational throughput for matrix workloads.
2Speed
If traditional memory access patterns are used, then general purpose computing is maintained, but processing speed deteriorates for large matrix datasets
Solution Approach 1:
By segmenting large matrices into tiles, the patent enables efficient caching and memory hierarchy utilization. Smaller tile blocks can be kept in faster cache memory, reducing memory access latency and improving processing speed for large matrix operations.
Solution Approach 2:
The patent implements preliminary loading of matrix tiles into processor registers and cache memory before computation begins. This preliminary action reduces the need for repeated memory accesses during computation, thereby improving processing speed.
Data Source
AI summary
Embodiments detailed herein relate to matrix (tile) operations. For example, decode circuitry to decode an instruction having fields for an opcode and a memory address, and execution circuitry to execute the decoded instruction to store configuration information about usage of storage for two-dimensional data structures at the memory address.