Tile Matrix Dot Product Instructions for 16-Bit Floating-Point Throughput
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Solution Overview
Problem
Existing processors face challenges in efficiently handling matrix operations, particularly for larger matrices, due to the instruction intensity and difficulty in managing rows of matrices within packed data registers.
Innovation Solution
The implementation of 2-dimensional data structures called tiles, which are used to perform matrix operations such as multiplication, addition, and dot products, along with specialized instructions like TILE16BDP, to optimize matrix computations using configured tiles and matrix operations accelerators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional packed data registers are used for matrix operations, then the processor can handle basic matrix computations, but the instruction intensity increases and efficiency decreases for larger matrices
Solution Approach 1:
The patent divides large matrices into smaller sub-matrices or tiles that can be processed independently and in parallel. This segmentation reduces the instruction intensity for each operation while maintaining overall computational throughput by allowing multiple tile operations to be executed simultaneously across different processor units.
Solution Approach 2:
The patent introduces a tile-based dimension to the traditional row-based matrix storage and processing model. By organizing data into 2D tiles with specific row and column dimensions, the system enables more efficient memory access patterns and allows for better utilization of vector processing units, thereby improving productivity without proportionally increasing instruction 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 matrix operation tiles that can be configured to perform multiple types of operations including multiplication, accumulation, and other linear algebra functions. This multi-functionality allows a single specialized hardware unit to handle various matrix computations, improving peak compute efficiency and energy utilization without requiring separate dedicated hardware for each operation type.
Solution Approach 2:
The patent employs configurable tile parameters such as tile size, data precision (e.g., 16-bit floating-point), and operation types that can be adjusted based on the specific computational requirements. This parameter configurability allows the specialized hardware to optimize its performance for different workloads, achieving high peak compute efficiency while managing hardware complexity through flexible configuration rather than fixed specialized circuits for every possible operation.
Data Source
AI summary
Disclosed embodiments relate to computing dot products of nibbles in tile operands. In one example, a processor includes decode circuitry to decode a tile dot product instruction having fields for an opcode, a destination identifier to identify a M by N destination matrix, a first source identifier to identify a M by K first source matrix, and a second source identifier to identify a K by N second source matrix, each of the matrices containing doubleword elements, and execution circuitry to execute the decoded instruction to perform a flow K times for each element (m, n) of the specified destination matrix to generate eight products by multiplying each nibble of a doubleword element (M,K) of the specified first source matrix by a corresponding nibble of a doubleword element (K,N) of the specified second source matrix, and to accumulate and saturate the eight products with previous contents of the doubleword element.