From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs
arXiv:2606.07586v1 Announce Type: new Abstract: Spatial neural processing units (NPUs) provide an energy-efficient platform for edge LLM inference, but efficiently deploying an LLM end-to-end on such hardware remains labor-intensive. Although AI coding agents have begun to lower this cost, existing studies have largely focused on single-kernel optimization rather than end-to-end LLM deployment on resource-constrained spatial NPUs. We present a two-stage methodology, instantiated on the AMD XDNA 2 NPU, that progresses from human-guided development to agent autonomy. In the first stage, we devel
The rapid advancement of LLMs and the increasing demand for energy-efficient edge AI deployment on specialized hardware like NPUs are driving innovation in autonomous agent development.
This development allows for more efficient, autonomous deployment of large language models on edge devices, reducing development costs and accelerating the adoption of AI in resource-constrained environments.
The shift from human-guided to agent-autonomous LLM deployment on spatial NPUs simplifies and accelerates the end-to-end integration process, making advanced AI more accessible.
- · AMD
- · AI software developers
- · Edge AI device manufacturers
- · Industries adopting edge AI
- · Manual AI deployment specialists
- · Inefficient AI hardware platforms
More widespread and efficient LLM deployment on edge devices.
Accelerated development of powerful, localized AI applications independent of cloud infrastructure.
Enhanced AI agency and automation across various sectors, potentially altering labor markets significantly.
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Read at arXiv cs.LG