Neural Network Weight Matrix Interleaving for Computation Load Balancing
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Solution Overview
Problem
The uneven distribution of non-zero parameters in the weight matrix of recurrent neural networks (RNNs) leads to inefficient computation and resource utilization due to uneven computation loads across processor cores, which reduces the overall efficiency of neural networks.
Innovation Solution
The method involves interleaving and partitioning the weight matrix to achieve even distribution of key weight parameters, allowing for efficient matrix multiplication and reducing computational complexity without degrading performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the weight matrix is sparsified by setting elements smaller than a preset threshold to zero, then the size of the weight matrix is reduced, but the distribution of non-zero parameters becomes uneven
Solution Approach 1:
The patent applies preliminary action by performing an initial sparsification step before the main iterative optimization process. This preliminary sparsification reduces the weight matrix size early in the training process, and subsequent iterations progressively improve the distribution uniformity of non-zero parameters without requiring complete retraining from scratch.
Solution Approach 2:
The patent implements dynamics by making the sparsification threshold adaptive rather than fixed. The threshold evolves during training iterations, adjusting dynamically to achieve better distribution uniformity of non-zero parameters across processor cores while maintaining the reduced matrix size benefit.
2Productivity
If the sparsification threshold is adjusted to achieve even distribution of non-zero parameters, then computation load becomes even on each processor core, but computation complexity increases
Solution Approach 1:
The patent applies partial action by implementing sparsification only for a portion of the weight matrix elements rather than all elements. By selectively applying sparsification to specific portions of the matrix based on the adaptive threshold, the system achieves improved computation efficiency without the full complexity cost of processing the entire matrix.
Solution Approach 2:
The patent segments the weight matrix processing into multiple iterations with different sparsification thresholds. Each iteration handles a segment of the optimization process, progressively improving distribution uniformity while keeping computational complexity manageable through incremental rather than monolithic processing.
3Productivity
If the sparsification threshold is adjusted to achieve even distribution of key weights, then computation load becomes balanced, but performance of the neural network may degrade
Solution Approach 1:
The patent implements feedback by using the neural network's performance and computation load distribution as feedback signals to adjust the sparsification threshold in subsequent iterations. This closed-loop approach ensures that threshold adjustments improve computation efficiency while monitoring and maintaining neural network performance through continuous evaluation and adaptation.
Solution Approach 2:
The patent applies parameter changes by dynamically modifying the sparsification threshold parameter during training iterations. The threshold is adjusted based on observed performance metrics and distribution uniformity, allowing the system to find an optimal balance between computation efficiency and neural network performance rather than using a fixed threshold.
Data Source
AI summary
A signal processing method and apparatus, where the apparatus includes an input interface configured to receive an input signal matrix and a weight matrix, a processor configured to interleave the input signal matrix to obtain an interleaved signal matrix, partition the interleaved signal matrix, interleave the weight matrix to obtain an interleaved weight matrix, process the interleaved weight matrix to obtain a plurality of sparsified partitioned weight matrices, perform matrix multiplication on the sparsified partitioned weight matrices and a plurality of partitioned signal matrices to obtain a plurality of matrix multiplication results, and an output interface configured to output a signal processing result.