AI Tensor Operations with Distributed Tiles for Memory Bottlenecks
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Solution Overview
Problem
Processing large tensors using a single processor is difficult due to memory bottlenecks and computational inefficiencies, necessitating improved methods for tensor operations.
Innovation Solution
Tensor parallelism is employed, where large tensors are split into smaller tiles and distributed across multiple processing devices for simultaneous execution, utilizing systolic arrays and data sharing techniques like data reduction and gather processes to optimize operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large tensors are processed using a single processor, then memory bottlenecks and computational inefficiencies occur, but distributing tensors across multiple processing devices requires complex data sharing and coordination mechanisms
Solution Approach 1:
The patent divides large tensors into smaller tensor tiles and distributes them across multiple processing devices. Each processing device handles a specific portion of the tensor computation, enabling parallel processing and improving computational efficiency while managing memory constraints effectively.
Solution Approach 2:
The patent introduces intermediary structures and coordination mechanisms to manage data sharing between processing devices. These intermediaries facilitate efficient communication and data exchange, reducing the complexity of direct peer-to-peer coordination while maintaining system-wide consistency.
2Quantity of substance
If tensors are split into multiple tiles distributed across processing devices, then memory bottlenecks are reduced, but data transfer and synchronization between devices increase processing overhead
Solution Approach 1:
The patent performs preliminary data preparation and organization before distribution. Tensor tiles are pre-processed and organized in an optimal format for distribution, reducing the need for complex data transformations during computation and minimizing data transfer overhead.
Solution Approach 2:
The patent transforms the data distribution strategy by organizing tensors in multi-dimensional tiles that can be efficiently distributed across processing devices. This dimensional organization allows for optimized data access patterns and reduces the overhead of data transfer and synchronization.
3Speed
If tensor operations are performed in parallel across multiple devices, then computational speed increases, but coordination and synchronization between devices become more complex
Solution Approach 1:
The patent merges the computational tasks across multiple processing devices into a unified parallel execution framework. By combining individual device operations into a coordinated parallel process, the system achieves high processing speed while managing coordination complexity through a unified control structure.
Solution Approach 2:
The patent dynamically adjusts computational parameters and data distribution strategies based on the specific tensor operation being performed. By changing parameters such as tile size, distribution pattern, and computation order, the system optimizes for both speed and coordination efficiency for different operation types.
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.