# Joint Quantization and Dynamic Partitioning for Context-Aware Edge Vision Transformers

**Authors:** Zhu Jiaqi, Jain Prakriti, Katare Dewant, Kumar Saurabh  
**Published:** 10 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.14196) · [PDF](https://arxiv.org/pdf/2611.14196) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.14196.html)

## Key numbers

- **34.0%**: end-to-end task completion latency is reduced by 34.0% on the Orin Nano and 33.1% on the…
- **33.1%**: …reduced by 34.0% on the Orin Nano and 33.1% on the Orin NX relative to the…

## Abstract

Deploying vision transformer models across connected and automated vehicles (CAVs) requires joint management of computation and Vehicle-to-Vehicle (V2V) communication. Static split-inference methods use fixed partition points and cannot adapt to time-varying link bandwidth, processing load, and concurrent multi-stream traffic. This paper proposes an edge orchestration framework that jointly selects model partition points, per-layer quantization bitwidths, and execution placement across heterogeneous vehicular compute nodes. The joint partitioning and mixed-precision allocation problem is formulated as a Mixed-Integer Linear Program (MILP) that minimizes compute and communication energy subject to end-to-end latency and task-quality constraints. The framework is evaluated on Jetson Orin Nano (8~GB), Jetson Orin NX (16~GB), and Jetson AGX Orin (32~GB) nodes using OPV2V-driven multi-stream workloads with K \in \2,4,6,8\. Across the evaluated configurations, the proposed formulation reduces V2V communication energy by $33.3$--36.0% relative to a Uniform INT8 split. At $K=8$, end-to-end task completion latency is reduced by 34.0% on the Orin Nano and 33.1% on the Orin NX relative to the same baseline, while limiting the reduction in 3D detection accuracy to $3.1$--$4.1$ percentage points in mAP_50 relative to FP16 execution.

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