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[2602.13052] Quantization-Aware Collaborative Inference for Large Embodied AI Models
Summary
This paper explores quantization-aware collaborative inference for large embodied AI models, addressing challenges in resource-limited environments by optimizing inference quality, latency, and energy consumption.
Why It Matters
As AI models grow in size and complexity, their deployment in resource-constrained settings becomes increasingly challenging. This research provides a framework for optimizing performance in embodied AI systems, which is crucial for applications in robotics and edge computing.
Key Takeaways
- Introduces a method for quantization-aware collaborative inference in AI models.
- Develops a tractable approximation for quantization-induced inference distortion.
- Establishes bounds on quantization rate and inference distortion.
- Proposes a joint design problem to minimize distortion while adhering to energy constraints.
- Validates the approach through simulations and real-world experiments.
Computer Science > Machine Learning arXiv:2602.13052 (cs) [Submitted on 13 Feb 2026] Title:Quantization-Aware Collaborative Inference for Large Embodied AI Models Authors:Zhonghao Lyu, Ming Xiao, Mikael Skoglund, Merouane Debbah, H. Vincent Poor View a PDF of the paper titled Quantization-Aware Collaborative Inference for Large Embodied AI Models, by Zhonghao Lyu and 4 other authors View PDF HTML (experimental) Abstract:Large artificial intelligence models (LAIMs) are increasingly regarded as a core intelligence engine for embodied AI applications. However, the massive parameter scale and computational demands of LAIMs pose significant challenges for resource-limited embodied agents. To address this issue, we investigate quantization-aware collaborative inference (co-inference) for embodied AI systems. First, we develop a tractable approximation for quantization-induced inference distortion. Based on this approximation, we derive lower and upper bounds on the quantization rate-inference distortion function, characterizing its dependence on LAIM statistics, including the quantization bit-width. Next, we formulate a joint quantization bit-width and computation frequency design problem under delay and energy constraints, aiming to minimize the distortion upper bound while ensuring tightness through the corresponding lower bound. Extensive evaluations validate the proposed distortion approximation, the derived rate-distortion bounds, and the effectiveness of the proposed joint...
