3 papers
cs.CL2025
LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design
Renjie Wei, Songqiang Xu, Linfeng Zhong +5
State space models (SSMs) like Mamba have recently attracted much attention. Compared to Transformer-based large language models (LLMs), Mamba achieves linear computation complexit…
cs.AR2025
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…
cs.AR2025
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…