Low-Rank Matrix Compression Hardware for AI Memory Bottlenecks
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Solution Overview
Problem
Existing compression techniques for AI workloads involving large matrices and tensors are insufficient to address the significant storage, bandwidth, and power requirements, leading to high costs.
Innovation Solution
Implement low-rank decomposition-based hardware compression methods for matrices and tensors, utilizing graphics processing units (GPUs) to optimize storage and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing compression techniques are used for AI workloads, then some cost reduction is achieved, but storage, bandwidth, and power requirements remain significantly high
Solution Approach 1:
The patent applies segmentation by dividing large matrices and tensors into smaller blocks that can be independently compressed using low-rank decomposition. Each block is processed separately, allowing for more efficient storage and retrieval while reducing overall memory bandwidth requirements and power consumption during AI operations.
Solution Approach 2:
The patent changes the fundamental parameter representation of matrices by transforming them into low-rank decomposed forms (e.g., using SVD or other decomposition methods). This parameter transformation reduces the storage capacity needed while enabling efficient reconstruction and computation, directly addressing both storage and power consumption issues.
2Loss of energy
If existing compression techniques are used for AI workloads, then some cost reduction is achieved, but bandwidth requirements remain significantly high
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing the decomposition factors of matrices in a compressed format before they are needed for AI operations. This pre-processing step reduces the bandwidth requirements during actual inference or training, as the system only needs to retrieve and reconstruct the decomposed forms rather than handling full-size matrices.
Solution Approach 2:
The patent transitions from storing matrices in their original high-dimensional form to storing them in a lower-dimensional decomposed form. This dimensional reduction allows for more efficient memory bandwidth utilization while maintaining the ability to reconstruct and use the data for AI computations.
3Loss of energy
If existing compression techniques are used for AI workloads, then some cost reduction is achieved, but processing efficiency is compromised
Solution Approach 1:
The patent introduces an intermediary decomposed representation of matrices that serves as a bridge between storage efficiency and processing efficiency. The low-rank decomposed form acts as an intermediary that can be efficiently stored and transmitted while enabling fast reconstruction and computation through optimized algorithms, thus maintaining processing efficiency while reducing power consumption.
Data Source
AI summary
Low-rank decomposition-based hardware compression of matrices and tensors is described. An example of an apparatus includes a computer memory to store data for processing, and one or more processing resources including one or more accelerators, the one or more accelerators including circuitry for processing of one or more matrices. The circuitry includes decomposition-based compression circuitry, the decomposition-based compression circuitry performing decomposition of one or more input matrices to generate components representing approximated versions of the one or more input matrices.