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Guilin Zhao

dblp:213/8919 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Electronic design automation · 59% Memory systems · 20% Hardware reliability and fault tolerance · 20%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
in-memory computing
1.012026
NoiseGuard: A Comprehensive Framework With Noise Modeling, Noise-Aware Training, and Noise Compensation for In-Memory Computing SoC · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2026
Electronic design automation › logic synthesis
FPGA synthesis
0.912025
A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025
Electronic design automation
hardware verification and test
0.912025
A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025
Electronic design automation
logic synthesis
0.912025
A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025
Electronic design automation › design automation tools › FPGA CAD
FPGA design tools
0.312025
A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025

Methods — techniques the papers use, named apart from their topics

noise-aware training · 1.0noise compensation · 1.0abstract syntax tree guidance · 0.9HDL code generation · 0.9
YearPublicationVenuePosition
2026 NoiseGuard: A Comprehensive Framework With Noise Modeling, Noise-Aware Training, and Noise Compensation for In-Memory Computing SoC
Guangyao Wang, Yizhe Chen, Yuexi Lv, Yuannuo Feng, Jenny Ma, Saiya Wang, Guilin Zhao, Yong Pei, Minghua Tang, Wang Kang 0001
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.8
2025 A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers
abstract
Field Programmable Gate Array (FPGA) logic synthesis compilers (e.g., Vivado, Iverilog, Yosys, and Quartus) are widely applied in Electronic Design Automation (EDA), such as the development of FPGA programs. However, defects (e.g. incorrect synthesis) in logic synthesis compilers may lead to unexpected behaviors in target applications, posing security risks. Therefore, it is crucial to thoroughly test logic synthesis compilers to eliminate such defects. Despite several Hardware Design Language (HDL) code generators (e.g., Verismith) having been proposed to find defects in logic synthesis compilers, the effectiveness of these generators is still limited by the simple code generation strategy and the monogeneity of the generated HDL code. This paper proposes EvoHDL, a novel method to generate syntax-valid HDL code for comprehensively testing FPGA logic synthesis compilers. EvoHDL can generate more complex and diverse defect-triggering HDL code (e.g., Verilog, VHDL, and SystemVerilog) by leveraging the guidance of abstract syntax tree and the extensive function block libraries of cyber-physical systems. Extensive experiments show that the diversity and defect-triggering capability of HDL code generated by EvoHDL are significantly better than the state-of-the-art method (i.e., Verismith). In three months, EvoHDL has reported 20 new defects–many of which are deep and important; 16 of them have been confirmed.
Shikai Guo, Guilin Zhao, Peiyu Zou, He Jiang 0001
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.3
2023 Multi-view rotating machinery fault diagnosis with adaptive co-attention fusion network
Sa Meng, Xiwei Qiu, Guilin Zhao
Eng. Appl. Artif. Intell.5