AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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ReHGNN is presented, the first hybrid digital/analog PIM architecture for HGNN inference, and a greedy heuristic algorithm is proposed to further improve the utilization of ReRAM resource.
This work proposes a validation methodology based on a novel concept of Trait-Type Constraint Graph (TTCG), a graph that captures semantic relationships between traits and types and enables the derivation of the Trait Constraint Space (TCS) for systematic exploration.
A load-aware GPU-based dynamic graph pattern matching scheme is proposed to make full use of GPU computing resources and a task overhead prediction model is proposed to guide task allocation to alleviate the load imbalance between multiple GPU devices.
Nemo provides a set of new techniques that enable fast task dispatch, cache-conscious task offloading without CPU modifications, and versatile support of custom data types to address the CPU-accelerator latency.
Experiments on tool-augmented agent workloads show that CoAct improves per-step execution efficiency and resource utilization while achieving competitive or superior task accuracy, demonstrating that contrastive online dispatch can expose substantial parallelism in LLM-agent workflows without retraining the underlying...
LLM agents solve complex tasks by executing multi-step workflows that interleave LLM inference with external tool calls, yet execution efficiency is often the dominant bottleneck in real deployments because LLM-generated workflows are typically chain-structured and inherently sequential, limiting parallelism and underu...
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