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cs.AR2026
FlexLLM: Composable HLS Library for Flexible Hybrid LLM Accelerator Design
Jiahao Zhang, Zifan He, Nicholas Fraser +3
We present FlexLLM, a composable High-Level Synthesis (HLS) library for rapid development of domain-specific LLM accelerators. FlexLLM exposes key architectural degrees of freedom…
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…