AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Prox is a two-stage training-free framework for sparse SwiGLU FFNs that outperforms training-free baselines at all sparsity levels, achieves up to a $1.99\times end-to-end decoding speedup at 70\% FFN sparsity, and is compatible with quantization and sparse attention.
The first systematic empirical study of defects introduced during this stage of deep learning compilers in TorchDynamo, the default DLC frontend for PyTorch 2, the most popular DL framework is conducted, using a domain-knowledge-enhanced LLM-aided methodology.
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