External Transpose Circuitry for Large Matrix Operations
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Solution Overview
Problem
Existing processors face challenges in efficiently handling large matrices due to the difficulty and instruction intensity of matrix operations, particularly in deep learning applications, where row-by-row operations become inefficient with larger matrices.
Innovation Solution
The implementation of a matrix operations accelerator with transpose circuitry that includes a plurality of transpose engines, capable of operating in parallel and serial loading modes, to efficiently transpose and process matrices, along with support for tile-based operations and floating-point operations using a two-dimensional grid of fixed-point processing elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If row-by-row operations are used for matrix processing, then simplicity of operation is maintained, but efficiency deteriorates with larger matrices
Solution Approach 1:
The patent divides large matrices into smaller tiles that can be processed in parallel. The matrix processing is segmented into tile-level operations, allowing multiple tiles to be handled simultaneously by different processing elements, thus improving efficiency while maintaining operational simplicity through standardized tile processing routines.
Solution Approach 2:
The patent introduces a new dimension of parallelism by organizing processing elements in a two-dimensional grid architecture. Instead of sequential row-by-row processing, the system processes multiple tiles simultaneously across the grid, adding spatial parallelism to overcome the efficiency limitations of traditional approaches.
2Productivity
If matrix operations are performed on large matrices, then processing capability is improved, but instruction intensity and difficulty increase
Solution Approach 1:
The patent replaces complex software-based matrix operation instructions with dedicated hardware circuitry. Specialized matrix operation circuits perform tile-based matrix multiplications and other operations directly in hardware, eliminating the need for lengthy instruction sequences and reducing the computational burden on the main processor.
3Speed
If transpose operations are performed efficiently, then data reorganization speed is improved, but hardware complexity increases
Solution Approach 1:
The patent designs transpose circuitry that can handle multiple transpose configurations through a unified hardware structure. The same circuit can perform different transpose operations by configuring the interconnection patterns, eliminating the need for separate dedicated circuits for each transpose type and reducing overall hardware complexity.
Solution Approach 2:
The patent implements dynamically configurable transpose circuits that can adapt their behavior based on the specific operation required. The hardware can reconfigure its data flow paths and processing modes to match the needs of different transpose operations, providing flexibility without requiring multiple fixed-function circuits.
Data Source
AI summary
Examples detailed herein at least include transpose circuitry that is external to a matrix operations accelerator. In some examples, the transpose circuitry at least includes a plurality of transpose engines to transpose a source matrix operand of a single instruction to generate a transposed source matrix, and control circuitry to direct the plurality of transpose engines to alternately operate in a parallel loading mode and a serial loading mode to generate the transposed source matrix, wherein the plurality of transposes engines and the control circuitry are at least a portion of transpose circuitry.