Papers › Quantisation and Pruning for Neural Network Compression and Regularisation
Quantisation and Pruning for Neural Network Compression and Regularisation
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Network Pruning | CIFAR-10 | MobileNet – Quantised | Inference Time (ms) | 4.74 | #2 of 4 | Archive leaderboard | report |
| Network Pruning | CIFAR-10 | AlexNet – Quantised | Inference Time (ms) | 5.23 | #3 of 4 | Archive leaderboard | report |
| Network Pruning | CIFAR-10 | ShuffleNet – Quantised | Inference Time (ms) | 23.15 | #4 of 4 | Archive leaderboard | report |
| Neural Network Compression | CIFAR-10 | ShuffleNet – Quantised | Size (MB) | 1.9 | #1 of 5 | Archive leaderboard | report |
| Neural Network Compression | CIFAR-10 | MobileNet – Quantised | Size (MB) | 2.9 | #2 of 5 | Archive leaderboard | report |
| Neural Network Compression | CIFAR-10 | AlexNet – Quantised | Size (MB) | 54.6 | #3 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections