Distributed Tensor Tiling for AI Memory Bottlenecks
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Solution Overview
Problem
Processing large tensors in a single processor is difficult and can lead to computational inefficiencies and memory bottlenecks, necessitating improved methods for tensor operations in deep learning models.
Innovation Solution
The method involves splitting input tensors into smaller tiles that are distributed across multiple processing devices, performing operations on these tiles in parallel, and sharing data using data reduction or gather processes to generate intermediate and final solution tensors without requiring all processing devices to hold the entire tensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large tensors are processed in a single processor, then computational completeness is maintained, but computational efficiency deteriorates and memory bottlenecks occur
Solution Approach 1:
The input tensor is divided into multiple smaller input tensor tiles that are distributed across multiple processing devices. Each processing device processes only its assigned tile portion, enabling parallel computation and avoiding the memory bottlenecks of single-processor processing while maintaining computational completeness through coordinated processing of all tiles.
2Productivity
If input tensors are split and distributed across multiple processing devices, then computational efficiency improves through parallel processing, but data sharing requirements increase
Solution Approach 1:
The patent extracts and performs reduction operations on specific portions of tensors at intermediate stages. By computing reduction results for each input tensor tile separately and combining these results, the system minimizes the volume of data that needs to be shared between processing devices, reducing communication overhead while maintaining accuracy.
3Productivity
If tensor operations are performed using distributed tiles, then memory bottlenecks are reduced, but operation complexity increases
Solution Approach 1:
The patent performs reduction operations on input tensor tiles before the main tensor operations. By pre-computing reduction results for each distributed tile and making these results available to all processing devices, the system eliminates the need for complex real-time data sharing during tensor operations, simplifying coordination while maintaining memory efficiency.
Data Source
AI summary
A method of performing computations for artificial intelligence models may include obtaining an input tensor based on an input to an artificial intelligence model and loading the input tensor into multiple processing devices. The input tensor may be split into multiple input tensor tiles that are distributed among the processing devices such that each of the processing devices does not include an entirety of the input tensor. The method may also include performing multiple tensor operations according to the artificial intelligence model to generate multiple intermediate tensors and an output tensor, one or more of the tensor operations performed using the input tensor.