Summary of A Simple Sparse Matrix Vector Multiplication Approach to Padded Convolution, by Zan Chaudhry
A Simple Sparse Matrix Vector Multiplication Approach to Padded Convolution
by Zan Chaudhry
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce an algorithm that efficiently represents convolutions with zero-padding and stride as a sparse transformation matrix. This is achieved through sparse matrix-vector multiplication (SpMV), which can be applied to vectorized inputs. The authors provide a theoretical contribution, deriving an explicit expression for the number of non-zero multiplications in convolutions with stride and padding. This insight has potential applications in leveraging sparsity in convolution operations. A proof-of-concept implementation is presented in Python, demonstrating performance on both CPU and GPU architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This algorithm can be used to improve efficiency in machine learning and signal processing tasks that rely heavily on convolutional algorithms. The researchers’ findings lay the groundwork for future advancements in exploiting sparsity to speed up these operations. |
Keywords
» Artificial intelligence » Machine learning » Signal processing
