AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper proposes VQVLA, an algorithm-hardware co-design framework that accelerates VLA inference by exploiting weight similarity and execution dynamics, and proposes a merged-centroid vectorized GEMM paradigm that operates on the codebook-index representation.
Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment. Although Dadu-Corki, a dedicated accelerator for efficient embodied AI, has been introduced, it does not exploit t...
PCoMoE is presented, a path-compositional execution framework that shifts MoE inference from coarse-grained expert selection to fine-grained path composition and achieves up to a 1.31x end-to-end inference speedup while enhancing model accuracy by 10%.
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