arXiv:2402.14212v4 Announce Type: replace 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 lost information lies in the cokernel of each layer's Jacobian. We define submersive networks -- networks
The continuous push for deeper and more complex neural networks necessitates innovations to overcome fundamental computational bottlenecks like memory limitations in backpropagation.
This research addresses a core limitation in AI training, potentially enabling significantly larger and more efficient neural networks, which can accelerate AI development in various domains.
The ability to train deeper AI models without substantial memory overhead for intermediate activations changes the fundamental constraints on network architecture and scale.
- · AI hardware manufacturers
- · Deep learning researchers
- · Companies with large AI models
- · Hardware developers focused solely on current backpropagation paradigms
More memory-efficient training allows for larger and deeper AI models without proportional increases in expensive high-bandwidth memory.
The development of 'submersive networks' could lead to new AI architectures that are inherently more scalable and adaptable.
Reduced computational constraints might accelerate the development of more general and capable AI, broadening its applications across industries.
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Read at arXiv cs.LG