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

31 citations · 70 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG202012 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.AR20206 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.AR202018 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.AR202031 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…

cs.CV20202 cited

FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications

Jieru Zhao, Tingyuan Liang, Liang Feng +4

Fast and accurate depth estimation, or stereo matching, is essential in embedded stereo vision systems, requiring substantial design effort to achieve an appropriate balance among…

cs.DC20191 cited

Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis

Jieru Zhao, Tingyuan Liang, Sharad Sinha +1

High-level synthesis (HLS) shortens the development time of hardware designs and enables faster design space exploration at a higher abstraction level. Optimization of complex appl…