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

31 citations · 109 across the 14 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

cs.LG2020★ 12 cited

Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and System

Zhe Lin, Sharad Sinha, Wei Zhang

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable f…

cs.AR2020★ 6 cited

Scalable Light-Weight Integration of FPGA Based Accelerators with Chip Multi-Processors

Zhe Lin, Sharad Sinha, Hao Liang +2

Modern multicore systems are migrating from homogeneous systems to heterogeneous systems with accelerator-based computing in order to overcome the barriers of performance and power…

cs.AR2020★ 11 cited

Decision Tree Based Hardware Power Monitoring for Run Time Dynamic Power Management in FPGA

Zhe Lin, Wei Zhang, Sharad Sinha

Fine-grained runtime power management techniques could be promising solutions for power reduction. Therefore, it is essential to establish accurate power monitoring schemes to obta…

cs.AR2020★ 18 cited

An Ensemble Learning Approach for In-situ Monitoring of FPGA Dynamic Power

Zhe Lin, Sharad Sinha, Wei Zhang

As field-programmable gate arrays become prevalent in critical application domains, their power consumption is of high concern. In this paper, we present and evaluate a power monit…

cs.LG2020★ 15 cited

Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA

Zhe Lin, Sharad Sinha, Wei Zhang

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable f…

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…