SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks (2302.04852v1)

Abstract: We provide a new efficient version of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware.

• The paper introduces a novel sparse backpropagation algorithm that reduces computational redundancy by leveraging weight sparsity.
• It employs a versatile design that adapts to various sparsity patterns and common network layers, including convolutional and linear layers.
• Empirical tests reveal significant runtime speedups on commodity hardware, benefiting both transfer learning and training from scratch.

The paper "SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks" introduces a novel algorithm designed to enhance the efficiency of the backpropagation process in neural networks with sparse weights. This approach is particularly significant given the increasing emphasis on sparsity to reduce computational cost and improve the scalability of neural networks.

Key Contributions

1. Sparse Backpropagation Algorithm:
   • The authors present a new version of the backpropagation algorithm tailored for neural networks with sparse weights. Unlike traditional backpropagation, which is computationally intensive for dense matrices, the SparseProp algorithm leverages the structure of sparsity to reduce redundancy in computation.
2. General Applicability:
   • One of the standout features of SparseProp is its versatility. It applies to various types of sparsity patterns (unstructured sparsity) and is compatible with common neural network layers, including convolutional layers and linear layers. This makes SparseProp broadly applicable across different neural network architectures.
3. Implementation on Commodity Hardware:
   • The authors provide an optimized, vectorized implementation of SparseProp that can run efficiently on standard CPUs. This is particularly noteworthy because most optimization efforts for neural networks typically focus on specialized hardware like GPUs or TPUs. By contrast, SparseProp can enhance performance on more accessible, commodity hardware, democratizing the benefits of efficient sparse training.
4. Empirical Results:
   • The paper includes end-to-end runtime experiments demonstrating significant speedups when using SparseProp. These experiments cover two scenarios:
     • Transfer Learning: Applying SparseProp to already-sparsified networks, highlighting its potential for improving pre-trained models.
     • Training from Scratch: Evaluating SparseProp's performance in training new sparse networks from the ground up.

Implications

The findings from this paper suggest that SparseProp could play a crucial role in making the training of sparse neural networks more efficient and accessible, even on hardware that is traditionally considered less capable for deep learning tasks. By providing a pathway to faster training times and lower computational costs, SparseProp addresses a significant bottleneck in deploying and experimenting with sparse neural networks.

Conclusion

Overall, "SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks" contributes a critical advancement in the field of neural network training. By creating a specialized backpropagation algorithm that exploits sparsity and can be efficiently executed on commodity CPUs, the authors pave the way for more practical and scalable applications of sparse neural networks across a variety of platforms. This work not only improves training efficiency but also broadens the accessibility of advanced neural network techniques by reducing reliance on specialized hardware.