Xingheng Li

dblp:415/5608 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Systems, architecture and hardware · 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
1 paper
Electronic design automation · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation
design space exploration
0.912025
From Flatland to Forest: Exploring Pareto-optimal Design through RTL Hierarchy Trees · DAC 2025
Electronic design automation
high-level synthesis
0.912025
From Flatland to Forest: Exploring Pareto-optimal Design through RTL Hierarchy Trees · DAC 2025
Electronic design automation › design space exploration
microarchitecture design space exploration
0.912025
From Flatland to Forest: Exploring Pareto-optimal Design through RTL Hierarchy Trees · DAC 2025

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

kernel method · 0.9clustering · 0.9
YearPublicationVenuePosition
2025 From Flatland to Forest: Exploring Pareto-optimal Design through RTL Hierarchy Trees
abstract
The growing complexity of modern hardware has created vast design spaces that are difficult to explore efficiently. Current design space exploration (DSE) methods treat designs as flat parameter vectors, failing to leverage the rich structural information inherent in hardware architectures. This paper presents a novel RTL hierarchy aware approach to microarchitecture DSE that exploits the natural structure of hardware designs. We propose an RTL hierarchy aware kernel that enables direct comparison of RTL hierarchies, preserving their structural characteristics. Our method incorporates module importance derived from hierarchical synthesis reports through a weighted kernel extension. Additionally, we introduce a clustering method that leverages the proposed kernel to identify distinct architectural patterns, enabling efficient parallel evaluation. Experimental results and ablation studies on a Gemmini-based RISC-V SoC demonstrate the superiority of our approach.
Donger Luo, Qi Sun 0002, Xingheng Li, Cheng Zhuo, Bei Yu 0001, Hao Geng
DAC3