18 citations · 29 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
Efficient yet Accurate End-to-End SC Accelerator Design
Meng Li, Yixuan Hu, Tengyu Zhang +4
Providing end-to-end stochastic computing (SC) neural network acceleration for state-of-the-art (SOTA) models has become an increasingly challenging task, requiring the pursuit of…
Post-Layout Simulation Driven Analog Circuit Sizing
Xiaohan Gao, Haoyi Zhang, Siyuan Ye +6
Post-layout simulation provides accurate guidance for analog circuit design, but post-layout performance is hard to be directly optimized at early design stages. Prior work on anal…
READ: Reliability-Enhanced Accelerator Dataflow Optimization using Critical Input Pattern Reduction
Zuodong Zhang, Renjie Wei, Meng Li +3
With the rapid advancements of deep learning in recent years, hardware accelerators are continuously deployed in more and more safety-critical applications such as autonomous drivi…