Kimessha Paupamah, Steven James, Richard Klein
Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered devices. In this paper, we investigate reducing the computational and memory requirements of neural networks through network pruning and quantisation. We examine their efficacy on large networks like AlexNet compared to recent compact architectures: ShuffleNet and MobileNet. Our results show that pruning and quantisation compresses these networks to less than half their original size and improves their efficiency, particularly on MobileNet with a 7x speedup. We also demonstrate that pruning, in addition to reducing the number of parameters in a network, can aid in the correction of overfitting.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Model Compression | CIFAR-10 | Size (MB) | 1.9 | ShuffleNet – Quantised |
| Model Compression | CIFAR-10 | Size (MB) | 2.9 | MobileNet – Quantised |
| Model Compression | CIFAR-10 | Size (MB) | 54.6 | AlexNet – Quantised |
| Network Pruning | CIFAR-10 | Inference Time (ms) | 4.74 | MobileNet – Quantised |
| Network Pruning | CIFAR-10 | Inference Time (ms) | 5.23 | AlexNet – Quantised |
| Network Pruning | CIFAR-10 | Inference Time (ms) | 23.15 | ShuffleNet – Quantised |
| 2D Classification | CIFAR-10 | Size (MB) | 1.9 | ShuffleNet – Quantised |
| 2D Classification | CIFAR-10 | Size (MB) | 2.9 | MobileNet – Quantised |
| 2D Classification | CIFAR-10 | Size (MB) | 54.6 | AlexNet – Quantised |