Tensor Contraction Conversion for Machine Learning Processing
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Solution Overview
Problem
Tensor convolution operations in deep learning techniques require significant memory and computing resources and often necessitate specialized libraries, limiting their efficiency and flexibility.
Innovation Solution
The technique reinterprets n-mode convolutions as tensor contractions, allowing for the conversion of tensor convolution operations into tensor contraction operations using existing tensor contraction libraries, which can be performed in constant time without additional data elements, thereby optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tensor convolution operations are performed using specialized tensor convolution libraries, then the operations can be executed, but memory and computing resources are consumed significantly
Solution Approach 1:
The patent transforms the operational parameters of tensor convolution by reinterpreting it as tensor contraction. This changes the mathematical formulation from convolution-specific operations to contraction operations, allowing the use of different computational libraries and reducing resource consumption while maintaining functional equivalence
Solution Approach 2:
The patent creates a computational equivalence between tensor convolution and tensor contraction. By copying the functional behavior of convolution through contraction operations, it enables the use of existing contraction libraries instead of requiring specialized convolution libraries, thereby reducing memory and computing resource requirements
2Reliability
If specialized tensor convolution libraries are used, then tensor convolution operations can be performed, but the system lacks flexibility and requires additional specialized software
Solution Approach 1:
The patent makes tensor contraction operations universal by showing they can perform the function of tensor convolution. This allows existing tensor contraction libraries to serve dual purposes - both their original contraction function and the convolution function, eliminating the need for separate specialized convolution libraries and increasing system versatility
Solution Approach 2:
Instead of trying to make general libraries perform convolution operations, the patent inverts the approach by showing that convolution can be expressed as contraction. This reversal allows existing contraction libraries to handle convolution tasks, thereby reducing the need for specialized software and improving flexibility
3Productivity
If tensor convolution operations are performed with existing resources, then processing speed is limited, but converting to tensor contraction operations enables constant time performance
Solution Approach 1:
The patent changes the operational parameters from convolution to contraction, which enables constant time performance. This parameter transformation allows the system to achieve O(1) complexity for certain operations, significantly improving processing speed without requiring additional hardware complexity
Data Source
AI summary
Apparatuses, systems, and techniques to convert between tensor convolution and tensor contraction operations. In at least one embodiment, one or more convolution operations are performed on image data by at least contracting one or more tensors to generate one or more feature maps.