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.
Comprehensive evaluations on OpenVLA and RDT across LIBERO and ManiSkill benchmarks show that SpecVLA reduces end-to-end latency significantly while preserving task success rate, and achieves real-time robotic manipulation with both high efficiency and reliability.
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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