Tiled 16-Bit Floating-Point Instructions for Matrix Throughput
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Solution Overview
Problem
Existing processors face challenges in efficiently handling matrix operations, particularly with larger matrices, due to the complexity and instruction intensity involved in matrix multiplication tasks, which can be inefficient and resource-intensive.
Innovation Solution
The implementation of 2-dimensional data structures called tiles, which are used to represent packed regions of memory, and specialized instructions such as the TILE16BDP for performing 16-bit floating-point matrix dot products, optimize matrix operations by configuring and operating on these tiles, including operations like multiplication, addition, and dot products, using hardware support in CPUs, GPUs, and accelerators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional matrix multiplication methods are used on general-purpose processors, then flexibility and adaptability are maintained, but computational efficiency and throughput are insufficient
Solution Approach 1:
The patent divides the matrix operation processing into specialized segments: a matrix multiplication unit dedicated to GEMM operations, separate instruction fetch/decode units for matrix operations versus general operations, and tiled data structures that segment large matrices into manageable blocks. This segmentation allows the processor to achieve high throughput for matrix operations without requiring the entire processor architecture to be specialized, thus resolving the contradiction between productivity improvement and device complexity.
2Productivity
If low precision arithmetic is used to maximize throughput, then computational speed increases, but measurement precision may be compromised
Solution Approach 1:
The patent implements support for multiple precision formats (16-bit, 32-bit, 64-bit floating point and integer types) and allows dynamic selection of precision levels based on application requirements. The matrix multiplication unit can process different data types with appropriate precision, enabling high-speed computation with 16-bit formats when sufficient while maintaining the option to use higher precision formats when numerical accuracy is critical, thus resolving the contradiction between productivity and measurement precision.
3Use of energy by moving object
If specialized hardware for matrix multiplication is implemented, then energy efficiency and peak compute improve, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The patent designs the matrix multiplication unit to be integrated within a general-purpose processor architecture, allowing the same hardware structure to serve both specialized matrix operations and general computational tasks. The unified design shares resources such as memory interfaces, register files, and control logic between matrix and non-matrix operations, reducing manufacturing complexity while maintaining energy efficiency for matrix workloads. This multi-functionality approach resolves the contradiction between energy efficiency improvement and ease of manufacture.
Data Source
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Figure 2(A)~2(C)
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AI summary
Disclosed embodiments relate to computing dot products of nibbles in tile operands. In one example, a processing unit comprises fetch circuitry to fetch an instruction, decode circuitry to decode the instruction, the instruction having a first field to specify a first storage location of a plurality of data elements corresponding to a first matrix having M rows by N columns of 32-bit single precision floating-point data elements, a second field to specify a second storage location of a plurality of data elements corresponding to a second matrix having M rows by K columns of pairs of 16-bit floating-point data elements having a bfloat16 format, and a third field to specify a third storage location of a plurality of data elements corresponding to a third matrix having K rows by N columns of pairs of 16-bit floating-point data elements having the bfloat16 format, and execution circuitry coupled with the decode circuitry, the execution circuitry to perform operations corresponding to the instruction.