Weighted Matrix for Neural Network Accelerator Memory Optimization
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Solution Overview
Problem
Neural network accelerators face memory inefficiencies and increased compilation times due to large compute graphs generated by iterative convolution operations, which hinder their performance in tasks like image recognition.
Innovation Solution
The approach involves flattening convolution filters into single-dimensional vectors and merging them to form a weighted matrix, allowing for efficient matrix-vector multiplication and reducing the complexity of convolution operations, thereby minimizing memory usage and enhancing computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative convolution operations are performed using traditional methods, then convolution accuracy is maintained, but memory utilization increases and compilation time increases
Solution Approach 1:
Multiple convolution filters are merged into a single weighted matrix by flattening each filter into a vector and concatenating them as rows. This combining approach reduces memory utilization by eliminating redundant storage of separate filter structures while maintaining convolution accuracy through mathematically equivalent operations.
2Measurement precision
If iterative convolution operations are performed using traditional methods, then convolution accuracy is maintained, but compilation time increases
Solution Approach 1:
The convolution filters are pre-flattened into vectors and merged into a weighted matrix before the convolution operation executes. This preliminary transformation simplifies the computational graph structure, reducing compilation time by eliminating the need to process and manage multiple separate filter definitions during the compilation phase.
3Adaptability or versatility
If multiple separate convolution filters are used, then filter specificity is maintained, but device complexity increases
Solution Approach 1:
Multiple convolution filters are merged into a single weighted matrix where each row corresponds to a specific filter's flattened weights. This approach maintains filter specificity by preserving all individual filter weights in the matrix structure while reducing device complexity by simplifying the computational graph to a single matrix-vector multiplication operation instead of multiple separate filter applications.
Data Source
AI summary
Examples of performing convolution operations based on a weighted matrix are described. In an example, an input data stream vector is processed using a weighted matrix stored onto a processing unit of a neural network accelerator. The weighted matrix may correspond to a first convolution filter and a second convolution filter.