Reducing Memory and Computational Cost for Deep Neural Network Training with Quantized Parameter Updates
For embedded devices, both memory and computational efficiency are essential due to their constrained resources. However, neural network training remains both computation and memory intensive. Although many existing studies apply quantization schemes to mitigate memory overhead, they often employ st...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Graz University of Technology
2025-08-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/164737/download/pdf/ |
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