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Found 7 papers, 3 papers with code
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
To address these challenges, researchers have developed various model compression techniques such as model quantization and model pruning.
Resource Efficient Neural Networks Using Hessian Based Pruning
We also present a full pipeline using EHAP and quantization aware training (QAT), using INT8 QAT to compress the network further after pruning.
Is Bio-Inspired Learning Better than Backprop? Benchmarking Bio Learning vs. Backprop
Four of the five Bio-algorithms tested outperform BP by upto 5% accuracy when only 20% of the training dataset is available.
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning
Surprisingly, we find that vanilla Global MP performs very well against the SOTA techniques.
PaRT: Parallel Learning Towards Robust and Transparent AI
Each subset consists of network segments, that can be combined and shared across specific tasks.
Global Magnitude Pruning With Minimum Threshold Is All We Need
We showcase that magnitude based pruning, specifically, global magnitude pruning (GP) is sufficient to achieve SOTA performance on a range of neural network architectures.
Learning to Prune Deep Neural Networks via Reinforcement Learning
PuRL achieves more than 80% sparsity on the ResNet-50 model while retaining a Top-1 accuracy of 75. 37% on the ImageNet dataset.
