Image de couverture de livre

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Makenali, Sara, Rokh, Babak, Azarpeyvand, Ali
Format:	Preprint
Publié:	2025
Sujets:	Neural and Evolutionary Computing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Adaptively Pruned Spiking Neural Networks for Energy-Efficient Intracortical Neural Decoding
par: Rivelli, Francesca, et autres
Publié: (2025)

A Hierarchical Importance-Guided Multi-objective Evolutionary Framework for Deep Neural Network Pruning
par: Khan, Zak, et autres
Publié: (2026)

Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning
par: Li, Yaxin, et autres
Publié: (2024)

Workload-Balanced Pruning for Sparse Spiking Neural Networks
par: Yin, Ruokai, et autres
Publié: (2023)

Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization
par: Deckers, Lucas, et autres
Publié: (2024)

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware
par: Chen, Zhenhui, et autres
Publié: (2025)

Pruning as Evolution: Emergent Sparsity Through Selection Dynamics in Neural Networks
par: Shah, Zubair, et autres
Publié: (2026)

Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning
par: Ma, Chenxiang, et autres
Publié: (2025)

Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks
par: Han, Bing, et autres
Publié: (2022)

LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
par: Liu, Qianhui, et autres
Publié: (2024)

Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks
par: Stapleton, Fergal, et autres
Publié: (2025)

MD-SNN: Membrane Potential-aware Distillation on Quantized Spiking Neural Network
par: Lee, Donghyun, et autres
Publié: (2025)

Integer-State Dynamics of Quantized Spiking Neural Networks for Efficient Hardware Acceleration
par: Zhang, Lei
Publié: (2026)

Competition-based Adaptive ReLU for Deep Neural Networks
par: Chen, Junjia, et autres
Publié: (2024)

CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
par: Huang, Yulong, et autres
Publié: (2024)

Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic Programming
par: Deng, Zihao, et autres
Publié: (2023)

Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition
par: Liu, Peiyu, et autres
Publié: (2025)

Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them
par: Guijt, Arthur, et autres
Publié: (2024)

Evaluating Deep Neural Networks in Deployment (A Comparative and Replicability Study)
par: Pinconschi, Eduard, et autres
Publié: (2024)

Efficient Event-based Delay Learning in Spiking Neural Networks
par: Mészáros, Balázs, et autres
Publié: (2025)

Parallel Spiking Unit for Efficient Training of Spiking Neural Networks
par: Li, Yang, et autres
Publié: (2024)

Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
par: Lv, Changze, et autres
Publié: (2024)

Evolving Efficient Genetic Encoding for Deep Spiking Neural Networks
par: Pan, Wenxuan, et autres
Publié: (2024)

FOXANN: A Method for Boosting Neural Network Performance
par: Jumaah, Mahmood A., et autres
Publié: (2024)

Scalable Dendritic Modeling Advances Expressive and Robust Deep Spiking Neural Networks
par: Huang, Yifan, et autres
Publié: (2024)

EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks
par: Poyatos, Javier, et autres
Publié: (2022)

Deep Neural Crossover
par: Shem-Tov, Eliad, et autres
Publié: (2024)

A Latency Coding Framework for Deep Spiking Neural Networks with Ultra-Low Latency
par: Lu, Yi, et autres
Publié: (2026)

Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
par: Qiu, Xuerui, et autres
Publié: (2023)

Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
par: Che, Kaiwei, et autres
Publié: (2024)

TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
par: Lee, Donghyun, et autres
Publié: (2024)

Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN
par: Chakraborty, Biswadeep, et autres
Publié: (2024)

Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance
par: Hussien, Mostafa, et autres
Publié: (2024)

Neural Architecture Search of Time-to-First-Spike-Coded Spiking Neural Networks for Efficient Eye-based Emotion Recognition
par: Liu, Qianhui, et autres
Publié: (2025)

Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods
par: Zhou, Chenlin, et autres
Publié: (2024)

Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks
par: Pes, Lorenzo, et autres
Publié: (2024)

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks
par: Zhang, Hangming, et autres
Publié: (2024)

Spiking Brain Compression: Post-Training Second-order Compression for Spiking Neural Networks
par: Shi, Lianfeng, et autres
Publié: (2025)

Deep Neural Network-guided PSO for Tracking a Global Optimal Position in Complex Dynamic Environment
par: Raharja, Stephen, et autres
Publié: (2026)

Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications
par: Aksoy, Alperen, et autres
Publié: (2025)