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CREATE: Cross-Layer Resilience Characterization and Optimization for Efficient yet Reliable Embodied AI Systems
Published in ASPLOS 2026, 2026
Characterizing the reliability of LLM-based embodied AI systems across the application, system, and circuit layers, with error detection and correction techniques for efficient yet reliable embodied AI.
Recommended citation: T. Xie*, Y. Qi*, J. Wen, Z. Wan, Y. Dong, Z. Wang, S. Cai, Y. Liang, T. Jia, Y. Wang, R. Wang, and M. Li. "CREATE: Cross-Layer Resilience Characterization and Optimization for Efficient yet Reliable Embodied AI Systems." International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2026. (*Equal contribution)
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Faster-MoA: Low-Latency Tree-Structured MoA Serving with Early Exit and Agent-Aware Prefill-Decode Overlap
Published in DAC 2026, 2026
Accelerating Mixture-of-Agents serving with a tree-structured aggregation architecture, semantic-similarity-based early exit, and agent-aware prefill-decode overlap — 10× faster inference with only ±1% accuracy variation.
Recommended citation: Z. Wang*, Y. Qi*, H. Chen, and Z. Wan. "Faster-MoA: Low-Latency Tree-Structured MoA Serving with Early Exit and Agent-Aware Prefill-Decode Overlap." Design Automation Conference (DAC), 2026. (*Equal contribution)
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DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation
Published in DAC 2026, 2026
Dynamic-static layer-skipping for vision-language-action models: adaptive layer bypass based on task requirements and motion significance, with 85.7× fewer trainable parameters than full fine-tuning.
Recommended citation: Z. Yang, Y. Qi, T. Xie, B. Yu, S. Liu, and M. Li. "DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation." Design Automation Conference (DAC), 2026.
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