6 papers · 1 filter
Aging Aware Adaptive Voltage Scaling for Reliable and Efficient AI Accelerators
Tong Xie, Zuodong Zhang, Chao Yang +3
Deep neural networks (DNNs) have showcased remarkable performance across various tasks and are widely deployed on AI accelerators fabricated in advanced technology nodes for effici…
DRIFT: Harnessing Inherent Fault Tolerance for Efficient and Reliable Diffusion Model Inference
Jinqi Wen, Tong Xie, Runsheng Wang +1
Diffusion model deployment has been suffering from high energy consumption and inference latency despite its superior performance in visual generation tasks. Dynamic voltage and fr…
The Quest for Reliable AI Accelerators: Cross-Layer Evaluation and Design Optimization
Meng Li, Tong Xie, Zuodong Zhang +1
As the CMOS technology pushes to the nanoscale, aging effects and process variations have become increasingly pronounced, posing significant reliability challenges for AI accelerat…
CREATE: Cross-Layer Resilience Characterization and Optimization for Efficient yet Reliable Embodied AI Systems
Tong Xie, Yijiahao Qi, Jinqi Wen +9
Embodied Artificial Intelligence (AI) has recently attracted significant attention as it bridges AI with the physical world. Modern embodied AI systems often combine a Large Langua…
SpecMamba: Accelerating Mamba Inference on FPGA with Speculative Decoding
Linfeng Zhong, Songqiang Xu, Huifeng Wen +4
The growing demand for efficient long-sequence modeling on edge devices has propelled widespread adoption of State Space Models (SSMs) like Mamba, due to their superior computation…
ReaLM: Reliable and Efficient Large Language Model Inference with Statistical Algorithm-Based Fault Tolerance
Tong Xie, Jiawang Zhao, Zishen Wan +5
The demand for efficient large language model (LLM) inference has propelled the development of dedicated accelerators. As accelerators are vulnerable to hardware faults due to agin…