arXiv subjects

Zhibai Huang

Publications and source records attributed to Zhibai Huang.

3 recordsLinked to original sources

arXiv2026

Jiao: Bridging Isolation and Customization in Mixed Criticality Robotics

Consumer robotics demands consolidation of safety-critical control, perception pipelines, and user applications on shared multicore platforms. While static partitioning hypervisors provide hardware-enforced isolation, directly transplanting automotive architectures encounters an expertise asymmetry problem in which end-users modifying robot behavior lack the systems knowledge that platform developers possess. We present an architecture addressing this challenge through three integrated components. A Safe IO Cell provides hardware-level override capability. A Parameter Synchronization Service encapsulates cross-domain complexity. A Safety Communication Layer implements IEC~61508-aligned verification. Our empirical evaluation on an ARM Cortex-A55 platform demonstrates that partition isolation reduces cycle-period jitter by 84.5\% and cuts tail timing error by nearly an order of magnitude (p99 $|$jitter$|$ from 69.0\,$μ$s to 7.8\,$μ$s), eliminating all $>$50\,$μ$s~excursions.

cs.RO↗

arXiv2026

The Phantom of PCIe: Constraining Generative Artificial Intelligences for Practical Peripherals Trace Synthesizing

Peripheral Component Interconnect Express (PCIe) is the de facto interconnect standard for high-speed peripherals and CPUs. The development of PCIe devices for emerging applications requires realistic Transaction Layer Packet (TLP) traces that accurately simulate device-CPU interactions. While generative AI offers a promising avenue for synthesizing complex TLP sequences, it is prone to a critical challenge inherent in all generation tasks: hallucination. Naively applying these models often produces traces that violate fundamental PCIe protocol rules, such as ordering and causality, rendering them unusable for device simulation. To resolve this, our work introduces a methodology to bridge the gap between generative AI and high-fidelity device simulation. This paper presents Phantom, a framework that systematically addresses AI-generated hallucinations in TLP synthesis. Phantom achieves this by coupling a generative backbone with a novel post-processing filter that enforces PCIe-specific constraints, effectively eliminating invalid TLP sequences. We validate Phantom's effectiveness by synthesizing TLP traces for an actual PCIe network interface card. Experimental results show that Phantom produces practical, large-scale TLP traces, significantly outperforming existing models, with improvements of up to 1000$\times$ in task-specific metrics and up to 2.19$\times$ in Fréchet Inception Distance (FID) compared to backbone-only methods. The prototype implementation has been made open-source.

cs.LG↗

arXiv2025

Equinox: Holistic Fair Scheduling in Serving Large Language Models

We address the limitations of current LLM serving with a dual-counter framework separating user and operator perspectives. The User Fairness Counter measures quality of service via weighted tokens and latency; the Resource Fairness Counter measures operational efficiency through throughput and GPU utilization. Since these metrics are only available post-execution, creating a scheduling paradox, we introduce a deterministic Mixture of Prediction Experts (MoPE) framework to predict user-perceived latency, output tokens, throughput, and GPU utilization. These predictions enable calculation of a unified Holistic Fairness score that balances both counters through tunable parameters for proactive fairness-aware scheduling. We implement this in Equinox, an open-source system with other optimizations like adaptive batching, and stall-free scheduling. Evaluations on production traces (ShareGPT, LMSYS) and synthetic workloads demonstrate Equinox achieves up to $1.3\times$ higher throughput, 60\% lower time-to-first-token latency, and 13\% higher fairness versus VTC while maintaining 94\% GPU utilization, proving fairness under bounded discrepancy across heterogeneous platforms.

cs.DC↗