arXiv:2606.15959v1 Announce Type: cross Abstract: Neural networks are used as generative surrogate models for scientific discovery, which are trainable approximations of scientific simulations. These models enable users to replace time-consuming numerical simulations with learned alternatives, providing quick solutions. However, high-fidelity generative surrogate models require massive training datasets, which can create storage and I/O challenges. Lossy compression is a promising way to reduce this burden, but compression errors may affect the model quality in subtle ways, making it challengi
The increasing scale and fidelity of neural generative models is pushing the limits of current computational storage and I/O capabilities, making lossy compression an urgent optimization challenge.
Improving the efficiency of generative AI through optimized data management directly impacts the scalability, cost, and accessibility of advanced AI workloads, potentially lowering barriers to entry for model development and deployment.
The focus for generative AI development shifts to incorporate data compression techniques as a critical component, balancing model fidelity with practical resource constraints.
- · AI model developers
- · Cloud providers
- · Data storage solution providers
- · Scientific discovery platforms
- · Inefficient data storage models
- · Legacy HPC systems
More efficient training and deployment of large-scale generative AI models for scientific and industrial applications.
Reduced infrastructure costs for AI research and development, accelerating innovation and potentially broadening access to advanced AI capabilities.
The development of new compression algorithms specifically tailored for scientific data, becoming a critical field within AI infrastructure.
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Read at arXiv cs.AI