activity
20192026
most citedHL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis

31 citations · 37 across the 12 of their papers we have counts for

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Showing cs.ARShow all

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cs.AR2024

Hierarchical Source-to-Post-Route QoR Prediction in High-Level Synthesis with GNNs

Mingzhe Gao, Jieru Zhao, Zhe Lin +1

High-level synthesis (HLS) notably speeds up the hardware design process by avoiding RTL programming. However, the turnaround time of HLS increases significantly when post-route qu…

cs.AR2024

An Optimizing Framework on MLIR for Efficient FPGA-based Accelerator Generation

Weichuang Zhang, Jieru Zhao, Guan Shen +3

With the increasing demand for computing capability given limited resource and power budgets, it is crucial to deploy applications to customized accelerators like FPGAs. However, F…

cs.AR2022★ 1 cited

AMF-Placer 2.0: Open Source Timing-driven Analytical Mixed-size Placer for Large-scale Heterogeneous FPGA

Tingyuan Liang, Gengjie Chen, Jieru Zhao +2

On modern field-programmable gate arrays (FPGAs), certain critical path portions of the designs might be prearranged into many multi-cell macros during synthesis. These movable mac…

cs.AR2022★ 1 cited

SALO: An Efficient Spatial Accelerator Enabling Hybrid Sparse Attention Mechanisms for Long Sequences

Guan Shen, Jieru Zhao, Quan Chen +3

The attention mechanisms of transformers effectively extract pertinent information from the input sequence. However, the quadratic complexity of self-attention w.r.t the sequence l…

cs.AR2020★ 31 cited

HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis

Zhe Lin, Jieru Zhao, Sharad Sinha +1

High-level synthesis (HLS) enables designers to customize hardware designs efficiently. However, it is still challenging to foresee the correlation between power consumption and HL…