Tong Xie

PhD Student (co-advised w/ Prof. Runsheng Wang, 2024)

Peking University

Interests
  • Efficient AI
Education
  • B.E. in Peking University, 2024

Publications

(2026). HE-Guard: Efficient Fault Recovery for Reliable Homomorphic Encryption Accelerators. In HPCA 2027.

(2026). Aging Aware Adaptive Voltage Scaling for Reliable and Efficient AI Accelerators. In International Symposium of Electronic Design Automation (ISEDA) 2026.

Paper

(2026). NICE: 3D-NAND-based In-Memory-Computing with In-Situ ECC-Protection for Fault-Tolerant and Efficient LLM Inference. In International Symposium of Electronic Design Automation (ISEDA) 2026.

(2026). CREATE: Cross-Layer Resilience Characterization and Optimization for Efficient yet Reliable Embodied AI Systems. In International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2026.

Paper

(2026). DRIFT: Harnessing Inherent Fault Tolerance for Efficient and Reliable Diffusion Model Inference. In Design Automation Conference (DAC) 2026.

Paper

(2026). DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation. In Design Automation Conference (DAC) 2026.

Paper

(2026). KEEP: A KV-Cache-Centric Memory Management System for Efficient Embodied Planning. In Design Automation Conference (DAC) 2026.

Paper

(2025). EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval. In Conference on Neural Information Processing Systems (NeurIPs) 2025.

Paper

(2025). SpecMamba: Accelerating Mamba Inference on FPGA with Speculative Decoding. In International Conference on Computer-Aided Design (ICCAD) 2025.

Paper

(2025). A 28nm 534.6TOPS/W Mixed-Precision Edge Accelerator for Embodied AI Using Stochastic Computing. In Asian Solid-State Circuits Conference (A-SSCC) 2025.

Paper

(2025). ReaLM: Reliable and Efficient Large Language Model Inference with Statistical Algorithm-Based Fault Tolerance. In Design Automation Conference (DAC) 2025.

Paper Project Slides

(2024). ASCEND: Accurate yet Efficient End-to-End Stochastic Computing Acceleration of Vision Transformer. In Design, Automation and Test in Europe Conference and Exhibition (DATE) 2024.

Paper