Summary of Joint Pruning and Channel-wise Mixed-precision Quantization For Efficient Deep Neural Networks, by Beatrice Alessandra Motetti et al.
Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks
by Beatrice Alessandra Motetti, Matteo Risso, Alessio Burrello, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed methodology jointly applies pruning and mixed-precision quantization to deep neural networks (DNNs) via a lightweight gradient-based search, resulting in optimized DNNs that achieve better accuracy-cost tradeoffs. The approach targets edge devices, addressing the significant challenges posed by resource requirements. By leveraging hardware-aware optimization, the methodology reduces the time required to generate Pareto-optimal DNNs. Tested on three benchmarks (CIFAR-10, Google Speech Commands, and Tiny ImageNet), the method achieves impressive results, including a 47.50% size reduction at iso-accuracy with all weights quantized at 8-bit and 69.54% at 2-bit. The methodology surpasses previous state-of-the-art approaches in terms of size reduction and training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make deep neural networks (DNNs) work better on devices like smartphones or smart speakers. These devices have limited resources, so the authors developed a method that combines two techniques: pruning and mixed-precision quantization. This allows DNNs to be smaller and faster while still being accurate. The method was tested on three different tasks and showed great results, making it a more efficient way to deploy DNNs. |
Keywords
* Artificial intelligence * Optimization * Precision * Pruning * Quantization
