Moonwalk: Inverse-Forward Differentiation
📰 ArXiv cs.AI
Moonwalk introduces Inverse-Forward Differentiation to reduce memory usage in deep neural networks by avoiding storage of intermediate activations
Action Steps
- Revisit the structure of gradient computation in backpropagation
- Identify the need to store intermediate activations as a limitation
- Apply Inverse-Forward Differentiation to compute gradients without storing activations
- Implement Moonwalk in deep learning frameworks to enable training of deeper networks
Who Needs to Know This
ML researchers and engineers on a team can benefit from Moonwalk as it enables training of deeper networks, and software engineers can implement this technique in deep learning frameworks
Key Insight
💡 Inverse-Forward Differentiation can avoid storing intermediate activations during the forward pass
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🚀 Moonwalk: Inverse-Forward Differentiation reduces memory usage in deep neural networks
Key Takeaways
Moonwalk introduces Inverse-Forward Differentiation to reduce memory usage in deep neural networks by avoiding storage of intermediate activations
Full Article
Title: Moonwalk: Inverse-Forward Differentiation
Abstract:
arXiv:2402.14212v2 Announce Type: replace-cross Abstract: Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth of trainable networks. This raises a fundamental question: can we avoid storing these activations? We address this by revisiting the structure of gradient computation. Backpropagation computes gradients through a sequence of vector-Jacobian products, an operation that is generally irreversible. The
Abstract:
arXiv:2402.14212v2 Announce Type: replace-cross Abstract: Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth of trainable networks. This raises a fundamental question: can we avoid storing these activations? We address this by revisiting the structure of gradient computation. Backpropagation computes gradients through a sequence of vector-Jacobian products, an operation that is generally irreversible. The
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