Matrix Operations Accelerator Tile Scheduling
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Solution Overview
Problem
Existing processor architectures face challenges in efficiently executing matrix operations, particularly for large matrices, due to the complexity and instruction intensity involved in handling matrices.
Innovation Solution
The implementation of a matrix operations accelerator circuit that utilizes a two-dimensional grid of processing element circuits, along with dispatch and scheduling circuitry, to efficiently perform matrix operations such as multiplication, addition, and accumulation using tiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional processor executes matrix operations using standard instruction sets, then the operations can be performed with existing hardware, but the execution time and instruction complexity increase significantly for large matrices
Solution Approach 1:
The matrix operations accelerator divides the matrix into tiles that can be processed in parallel across multiple processing elements. Each processing element handles a specific tile, enabling simultaneous computation across different segments of the matrix, thereby increasing throughput and reducing execution time for large matrices.
Solution Approach 2:
The patent transitions from sequential scalar processing to a two-dimensional grid of processing elements that operate simultaneously. This spatial dimensionality allows multiple matrix operations to execute in parallel, dramatically improving productivity while reducing the time loss associated with sequential execution of traditional instruction sets.
2Productivity
If matrix operations are performed using standard processor instructions, then the implementation is simple and uses existing hardware, but the energy consumption increases for bulk data processing tasks
Solution Approach 1:
By segmenting the matrix into tiles and assigning them to different processing elements, the system performs bulk data processing in parallel. This segmentation enables efficient utilization of computational resources, improving compute efficiency while reducing redundant operations and associated energy consumption compared to sequential processing of standard instructions.
Solution Approach 2:
The specialized matrix operations accelerator creates an optimized computational environment specifically designed for matrix mathematics. This dedicated hardware architecture eliminates the overhead and inefficiencies of general-purpose processors, providing an 'inert' environment that is perfectly suited for matrix operations, thereby improving compute efficiency and reducing energy consumption.
3Productivity
If a matrix operations accelerator is implemented, then compute and energy efficiency are enhanced, but the device complexity increases with additional processing elements and control circuitry
Solution Approach 1:
The matrix operations accelerator is segmented into multiple identical processing elements arranged in a grid. This modular segmentation allows the system to achieve high peak compute through parallelism while managing complexity by repeating standardized units rather than designing complex custom circuits for each function.
Solution Approach 2:
Each processing element in the grid is designed to be universal and multi-functional, capable of performing various matrix operations (multiplication, accumulation, etc.). This universality reduces overall device complexity by using identical reusable units rather than requiring specialized circuitry for each operation type.
4Ease of operation
If matrix operations are executed using traditional processors, then the instruction set can handle various operations, but the instruction intensity and operational complexity increase
Solution Approach 1:
The patent extracts matrix operation functionality from the general-purpose instruction set and implements it in dedicated hardware. This extraction simplifies the operational model by providing specialized instructions that directly map to hardware capabilities, reducing instruction complexity and making matrix operations easier to execute compared to using traditional processors.
Data Source
AI summary
Systems, methods, and apparatuses relating to a matrix operations accelerator are described. In one embodiment, a processor includes a matrix operations accelerator circuit that includes a two-dimensional grid of fused multiply accumulate circuits that is switchable to a scheduling mode for execution of a decoded single instruction where the matrix operations accelerator circuit loads a first buffer of the two-dimensional grid of fused multiply accumulate circuits from a first plurality of registers that represents a first input two-dimensional matrix, checks if a second buffer of the two-dimensional grid of fused multiply accumulate circuits stores an immediately prior input two-dimension matrix that is the same as a second input two-dimensional matrix from a second plurality of registers that represents the first input two-dimensional matrix, and when the second buffer of the two-dimensional grid of fused multiply accumulate circuits stores the immediately prior input two-dimension matrix, from execution of a previous instruction, that is the same as the second input two-dimensional matrix: prevents reclamation of the second buffer between execution of the previous instruction and the decoded single instruction, performs an operation on the first input two-dimensional matrix from the first buffer and the immediately prior input two-dimension matrix from the second buffer to produce a resultant, and stores the resultant in resultant storage, and when the second buffer of the two-dimensional grid of fused multiply accumulate circuits does not store the immediately prior input two-dimension matrix, from execution of the previous instruction, that is the same as the second input two-dimensional matrix: loads the second input two-dimensional matrix into the second buffer of the two-dimensional grid of fused multiply accumulate circuits, performs the operation on the first input two-dimensional matrix from the first buffer and the second input two-dimension matrix from the second buffer to produce a resultant, and stores the resultant in the resultant storage.