most citedLUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs

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cs.AR2025★ 1 cited

LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs

Zifan He, Shengyu Ye, Rui Ma +2

The rapid development of large language models (LLM) has greatly enhanced everyday applications. While many FPGA-based accelerators, with flexibility for fine-grained data control,…

cs.AR2025

InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data Volume Variation in DNNs

Zifan He, Anderson Truong, Yingqi Cao +1

The rise of deep neural networks (DNNs) has driven an increased demand for computing power and memory. Modern DNNs exhibit high data volume variation (HDV) across tasks, which pose…