Demo Data from EDBT 2026 / reviewers in the wild / expert
Xinglong Yan
dblp:431/5417
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0009-9571-1916ORCID · reported
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 · 83% Memory systems · 17% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation › physical design
floorplanning |
1.0 | 1 | 2026 | DAMIL-DCIM+: Automated Dataflow-Aware Layout Synthesis for Digital CIM With Self-Assembled Bitcell Units and MILP-Based Optimization · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2026 |
Electronic design automation
physical design |
1.0 | 1 | 2026 | DAMIL-DCIM+: Automated Dataflow-Aware Layout Synthesis for Digital CIM With Self-Assembled Bitcell Units and MILP-Based Optimization · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2026 |
Electronic design automation › physical design
placement |
1.0 | 1 | 2026 | DAMIL-DCIM+: Automated Dataflow-Aware Layout Synthesis for Digital CIM With Self-Assembled Bitcell Units and MILP-Based Optimization · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2026 |
Memory systems › processing-in-memory › computing-in-memory
digital compute-in-memory |
0.3 | 1 | 2026 | DAMIL-DCIM+: Automated Dataflow-Aware Layout Synthesis for Digital CIM With Self-Assembled Bitcell Units and MILP-Based Optimization · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2026 |
Memory systems
processing-in-memory |
0.3 | 1 | 2026 | DAMIL-DCIM+: Automated Dataflow-Aware Layout Synthesis for Digital CIM With Self-Assembled Bitcell Units and MILP-Based Optimization · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2026 |
Methods — techniques the papers use, named apart from their topics
template-based placement · 1.0self-assembled bitcell units · 1.0mixed integer linear programming · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | DAMIL-DCIM+: Automated Dataflow-Aware Layout Synthesis for Digital CIM With Self-Assembled Bitcell Units and MILP-Based OptimizationabstractDigital computing-in-memory (DCIM) systems integrate complex digital logic with parasitic-sensitive bitcell arrays, presenting unique physical design challenges. Conventional design strategies often fall short in these systems due to irregular dataflow patterns and excessive interconnect lengths, which degrade performance and increase parasitic effects. As a result, current DCIM implementations frequently rely on manual layout, which is both time-consuming and a major bottleneck in the design cycle. While existing DCIM layout synthesis frameworks attempt to automate this process using template-based placement methods inspired by manual design, their rigid constraints can lead to inefficient area utilization and increased core sizes. To address these limitations, we propose DAMIL-DCIM+, a novel placement framework that combines the structural clarity of template-based methods with the flexibility of optimization-based techniques. Specifically, DAMIL-DCIM+ employs a global dataflow-aware floorplan to guide placement and leverages MILP-based detailed placement to optimize wirelength and preserve dataflow regularity. Inspired by self-assembling design principles, this approach enables scalable and structured integration of parasitic-sensitive components. The hybrid methodology of DAMIL-DCIM+ reduces total wirelength, lowers parasitic effects, and enhances performance while maintaining design regularity. Experimental results on a 28nm DCIM circuit demonstrate that DAMIL-DCIM+ improves operating frequency by 25.2% and reduces power consumption by 19.6% compared to Cadence Innovus, without increasing core area. Xinglong Yan, Zecheng Xu, Keren Zhu 0001, Shuo Li 0008, Fan Yang 0001, Xuan Zeng 0001 |
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. | 2 |