Exploring Parallelism in FPGA-Based Accelerators for Machine Learning Applications
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914158872625152 |
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| author | Centeno, Sed Sprague, Christopher Purkayastha, Arnab A Simar, Ray Magotra, Neeraj |
| author_facet | Centeno, Sed Sprague, Christopher Purkayastha, Arnab A Simar, Ray Magotra, Neeraj |
| contents | Speculative backpropagation has emerged as a promising technique to accelerate the training of neural networks by overlapping the forward and backward passes. Leveraging speculative weight updates when error gradients fall within a specific threshold reduces training time without substantially compromising accuracy. In this work, we implement speculative backpropagation on the MNIST dataset using OpenMP as the parallel programming platform. OpenMP's multi-threading capabilities enable simultaneous execution of forward and speculative backpropagation steps, significantly improving training speed. The application is planned for synthesis on a state-of-the-art FPGA to demonstrate its potential for hardware acceleration. Our CPU-based experimental results demonstrate that speculative backpropagation achieves a maximum speedup of 24% in execution time when using a threshold of 0.25, and accuracy remaining within 3-4% of the baseline across various epochs. Additionally, when comparing individual step execution time, speculative backpropagation yields a maximum speedup of 35% over the baseline, demonstrating the effectiveness of overlapping forward and backward passes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Parallelism in FPGA-Based Accelerators for Machine Learning Applications Centeno, Sed Sprague, Christopher Purkayastha, Arnab A Simar, Ray Magotra, Neeraj Distributed, Parallel, and Cluster Computing Hardware Architecture Machine Learning Speculative backpropagation has emerged as a promising technique to accelerate the training of neural networks by overlapping the forward and backward passes. Leveraging speculative weight updates when error gradients fall within a specific threshold reduces training time without substantially compromising accuracy. In this work, we implement speculative backpropagation on the MNIST dataset using OpenMP as the parallel programming platform. OpenMP's multi-threading capabilities enable simultaneous execution of forward and speculative backpropagation steps, significantly improving training speed. The application is planned for synthesis on a state-of-the-art FPGA to demonstrate its potential for hardware acceleration. Our CPU-based experimental results demonstrate that speculative backpropagation achieves a maximum speedup of 24% in execution time when using a threshold of 0.25, and accuracy remaining within 3-4% of the baseline across various epochs. Additionally, when comparing individual step execution time, speculative backpropagation yields a maximum speedup of 35% over the baseline, demonstrating the effectiveness of overlapping forward and backward passes. |
| title | Exploring Parallelism in FPGA-Based Accelerators for Machine Learning Applications |
| topic | Distributed, Parallel, and Cluster Computing Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2511.11640 |