A-IO: Adaptive Inference Orchestration for Memory-Bound NPUs
During the deployment of Large Language Models (LLMs), the autoregressive decoding phase on heterogeneous NPU platforms (e.g., Ascend 910B) faces severe memory-bound challenges. This study reveals the ``Model Scaling Paradox'' caused by the static deployment of single-sized models. It also points out the kernel synchronization overhead of fine-grained speculative decoding \cite{leviathan2023fast, chen2023speculative} under NPU computational graph compilation, and the severe limitations of purely relying on micro-level acceleration algorithms like Prompt LookUp Decoding (PLD)
| Authors | Chen Zhang, Yan Ding, Haotian Wang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 10 April 2026 |
| Last updated at source | 15 April 2026 |
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