Structured-Sparse Tile Matrix FMA for Faster Processor Execution
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Solution Overview
Problem
Existing computer processors face challenges in efficiently handling matrix operations, particularly for larger matrices, due to the complexity and instruction intensity involved in matrix manipulation tasks, which can lead to suboptimal performance in applications like deep learning.
Innovation Solution
The implementation of 2-dimensional data structures called tiles, which are configured to facilitate matrix operations through mechanisms such as matrix (tile) multiplication, addition, and accumulation, utilizing hardware support for packed data registers and fused multiply accumulate operations, enabling efficient handling of larger matrices.
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 throughput and energy efficiency deteriorate
Solution Approach 1:
The processor architecture is segmented into distinct functional units: general-purpose cores for control and specialized matrix processing units (MPUs) for computational tasks. Matrices are further segmented into tiles that can be processed in parallel across multiple MPUs, enabling high throughput while keeping individual processing units relatively simple.
Solution Approach 2:
A tile-based intermediate representation is introduced as a mediator between general-purpose processor instructions and hardware execution. The tile format serves as an intermediary data structure that enables efficient mapping to parallel hardware operations, bridging the gap between software flexibility and hardware acceleration.
2Speed
If matrix operations are performed using standard instruction sequences, then compatibility with existing software is maintained, but execution time and computational overhead increase
Solution Approach 1:
Matrices are pre-processed and organized into tile formats before being passed to the matrix processing units. This preliminary organization of data into appropriate tile structures enables the hardware to execute operations directly without additional processing overhead during computation, significantly reducing execution time.
Solution Approach 2:
The processor supports multiple tile configurations with different dimensions and data types (e.g., FP32, FP16, INT8). By changing the tile parameters to match the specific computational requirements, the system optimizes execution speed for different matrix operation scenarios while maintaining compatibility through a unified instruction interface.
3Measurement precision
If higher precision floating-point operations are used, then computational accuracy is improved, but energy consumption and processing time increase
Solution Approach 1:
The matrix processing units dynamically adapt their precision based on the input data characteristics and computational requirements. The system can switch between different precision modes (FP32, FP16, INT8) for different tiles or even different operations within the same matrix multiplication, optimizing the balance between accuracy and energy consumption for each specific computational task.
Solution Approach 2:
Different tile configurations support different data type precisions. By changing the precision parameter of the tile format, the system can perform high-precision operations when needed and low-precision operations when sufficient, reducing overall energy consumption while maintaining required accuracy levels for deep learning workloads.
Data Source
Figure 1A~1B
Figure 2(A)~2(C)
Figure 3
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 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.