Neural Network Parameter Decoding with Tensor Dimension Reordering
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Solution Overview
Problem
Existing frameworks lack the flexibility to adapt structured parameter representations, such as tensors, between processing steps, leading to inefficiencies in coding, decoding, and processing of neural network parameters.
Innovation Solution
A decoder and encoder system that performs tensor dimension reordering by shifting a single dimension, allowing for improved coding efficiency, flexibility, and complexity management in neural network parameter processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tensor dimension reordering is performed to improve coding efficiency, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic tensor dimension reordering where the reordering operation is performed conditionally based on processing requirements. The system can adaptively reorder dimensions during encoding/decoding operations when beneficial, rather than maintaining a fixed dimension order, thus improving coding efficiency without permanently increasing system complexity
Solution Approach 2:
The patent introduces a new dimension ordering perspective by reorganizing tensor dimensions along different axes. This allows the same tensor data to be processed with improved coding efficiency by viewing it through a different dimensional arrangement, achieving productivity improvement without adding physical complexity to the device
2Adaptability or versatility
If tensor dimension reordering is performed to improve flexibility, then flexibility is improved, but device complexity increases
Solution Approach 1:
The system provides dynamic adaptability by allowing tensor dimension reordering operations to be applied selectively based on the specific processing task. This enables the framework to adapt to different coding scenarios and tensor shapes without requiring a permanently complex reordering mechanism, achieving flexibility with minimal complexity overhead
Solution Approach 2:
The patent creates a universal tensor handling mechanism that can work with any tensor shape and dimension configuration. The reordering operation serves multiple functions including improving coding efficiency, adapting to different neural network architectures, and facilitating various processing operations, thereby achieving high flexibility without proportionally increasing device complexity
Data Source
AI summary
Embodiments according to the invention relate to a decoder for providing decoded parameters of a neural network on the basis of an encoded representation, wherein the decoder is configured to obtain a first multi-dimensional array comprising a plurality of neural network parameter values using a decoding of neural network parameters and wherein the decoder is configured to obtain a re-ordered multidimensional array using a reordering, in which a first dimension of the first multi-dimensional array is rearranged to a different dimension in the re-ordered multidimensional array. Furthermore, encoders, methods and computer programs using a reordering are disclosed.