AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Using neuro-symbolic AI as an end-to-end case study, it is shown that co-designing the full software-to-silicon stack yields orders-of-magnitude efficiency gains while preserving the accuracy and interpretability that make cognitive AI compelling.
This paper introduces 𝑆 2 Loop, a framework that introduces a flexible, all-level partitioning based AuthBlocks for ensuring full coverage of the entire design space, a realistic layout-based memory model, and an Authentication-Layout-Mapping co-search algorithm to explore the drastic combinatorial design space to figu...
DynamoServe is presented, a multi-tenant LLM serving framework that addresses challenges through three key innovations: leveraging stranded GPU memory to offload model weights and KV caches, mitigating resource fragmentation in multi-workload environments, and improving memory locality through coordinated data placemen...
TRACE-Router is presented, a task-level routing framework that aligns routing with the unit of supervision, and learns routing policies that adapt to the workload while avoiding explicit task-complexity estimation.
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