TLDEXPAND Instruction for Compressed Matrix Tile Loading
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Solution Overview
Problem
Current computer processor architectures face performance bottlenecks when handling large weight matrices in artificial intelligence systems due to limited memory bandwidth, especially in small batch size AI tasks, which can be exacerbated by the sparsity of these matrices, leading to increased Total Cost of Ownership and inefficiencies.
Innovation Solution
The introduction of a processor instruction, such as the TLDEXPAND instruction, that enables the loading and partial decompression of compressed matrix data, utilizing advanced vector extension (AVX) and matrix extension (AMX) technologies to improve compute efficiency and bandwidth utilization, specifically designed to handle quantized neural network weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If memory bandwidth is increased to handle large weight matrices, then performance is improved, but Total Cost of Ownership increases
Solution Approach 1:
The patent divides the weight matrix into tiles that can be compressed and stored more efficiently. By segmenting the large matrix into smaller manageable tiles, the system reduces the total memory bandwidth requirements while maintaining computational performance through selective loading and processing of these compressed tile segments.
Solution Approach 2:
The patent applies compression techniques that change the representation parameters of matrix data. By transforming weight matrices into compressed formats (changing density, encoding schemes, and storage parameters), the system achieves the same computational performance with reduced memory bandwidth requirements, thereby lowering Total Cost of Ownership.
2Quantity of substance
If compressed matrix data is used, then memory bandwidth is reduced, but decompression overhead is introduced
Solution Approach 1:
The patent performs compression of weight matrices in advance during model training or deployment preparation. By pre-compressing the matrix data before inference workloads, the system eliminates decompression overhead during actual computation, as the compressed tiles can be directly loaded and utilized without real-time decompression operations.
Solution Approach 2:
The patent introduces an intermediary compressed representation format that serves as a bridge between storage and computation. This intermediate format allows efficient memory bandwidth utilization while enabling direct computation on compressed data, avoiding the time cost of full decompression operations.
3Quantity of substance
If sparsity is utilized in weight matrices, then storage efficiency is improved, but processing complexity increases
Solution Approach 1:
The patent segments sparse weight matrices into tiles with uniform sparsity patterns. This segmentation allows the system to apply specialized compression techniques to each tile based on its sparsity characteristics, improving storage efficiency while managing processing complexity through localized rather than global complexity management.
Solution Approach 2:
The patent applies different compression and storage strategies to different regions (tiles) of the weight matrix based on local sparsity characteristics. By tailoring the compression approach to local properties rather than applying a uniform method globally, the system achieves improved storage efficiency without excessive processing complexity.
Data Source
AI summary
Techniques and mechanisms for processor circuitry to execute a load and expand instruction of an instruction set to generate decompressed matrix data. In an embodiment, the instruction comprises a source operand which indicates a location from which compressed matrix data, and corresponding metadata, are to be accessed. A destination operand of the instruction indicates a location which is to receive decompressed metadata, which is generated, during execution of the instruction, based on the compressed matrix data and the corresponding metadata. The metadata comprises compression mask information which identifies which elements of the matrix have been masked from the compressed matrix data. In another embodiment, the instruction further comprises a count operand which identifies a total number of the unmasked matrix elements which are represented in the compressed matrix data.