Composable Neural Network Kernels for Tensor Operations
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Solution Overview
Problem
Modern neural networks face challenges in achieving efficient computation due to the complexity of tensor operations and varying dimensionalities, sizes, and memory data layouts, which complicates hardware and software optimization across different hardware platforms.
Innovation Solution
A technique for manipulating generic tensors is introduced, allowing for operations on tensor descriptors and raw data, including slicing, merging, embedding, and reordering, to facilitate efficient computation by converting operations like convolution into General Matrix Multiplication (GEMM), enabling flexible and efficient processing across various hardware architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex tensor operations with varying dimensionalities and data layouts are performed directly, then computational accuracy is maintained, but computational efficiency and hardware adaptability deteriorate
Solution Approach 1:
The patent segments complex tensor operations into a standardized sequence of primitive operations (transposition, permutation, slicing, merging, embedding). Each primitive operation is independently optimized and can be efficiently mapped to different hardware architectures, thereby improving computational efficiency while maintaining manageable operation complexity through modular decomposition.
Solution Approach 2:
The patent creates a universal tensor operation framework that can handle various tensor operations (convolution, matrix multiplication, etc.) through a common set of primitive operations. This universal approach allows the same operation sequence to be efficiently executed on different hardware platforms (CPU, GPU, FPGA, ASIC), improving productivity across diverse hardware without requiring separate optimized implementations for each operation type.
2Speed
If specialized hardware is designed for specific neural network operations, then computational speed is improved, but hardware versatility and adaptability to different operations deteriorate
Solution Approach 1:
The patent develops a universal tensor operation framework where a single hardware architecture can efficiently execute multiple types of neural network operations through standardized primitive operations. The framework translates diverse operations (convolution, GEMM, etc.) into a common intermediate representation that can be executed on the same hardware, achieving both high speed through optimization and high versatility through adaptability to different operation types.
Solution Approach 2:
The patent uses parameter-based configuration to adapt the same hardware architecture to different operations. By changing parameters such as tensor dimensions, data layouts, and operation sequences in the standardized framework, the hardware can efficiently execute different neural network operations without physical reconfiguration, maintaining both speed and versatility.
3Productivity
If tensor operations are optimized for specific hardware architectures, then performance on that hardware is improved, but portability to other hardware platforms deteriorates
Solution Approach 1:
The patent creates a hardware-agnostic tensor operation framework that defines standardized primitive operations and a common intermediate representation. This universal framework can be compiled or translated to target different hardware architectures (CPU, GPU, FPGA, ASIC) while maintaining optimized performance on each platform. The separation between the universal operation definition and hardware-specific implementation enables both high performance and broad portability.
4Speed
If high-dimensional tensor operations are performed with detailed data layout control, then computational precision is maintained, but memory access efficiency and processing speed deteriorate
Solution Approach 1:
The patent segments high-dimensional tensor operations into lower-dimensional primitive operations that operate on smaller data subsets with simpler layouts. By decomposing complex high-dimensional operations into sequences of transposition, slicing, and merging operations on lower-dimensional tensors, the framework achieves better memory access patterns and processing speed while maintaining computational precision through the systematic composition of these segmented operations.
Data Source
AI summary
A technique for manipulating a generic tensor is provided. The technique includes receiving a first request to perform a first operation on a generic tensor descriptor associated with the generic tensor, responsive to the first request, performing the first operation on the generic tensor descriptor, receiving a second request to perform a second operation on generic tensor raw data associated with the generic tensor, and responsive to the second request, performing the second operation on the generic tensor raw data.