EDBT 2026 Demo / reviewers in the wild / expert
Wen-Tse Chang
dblp:415/5384
· DBLP profile ↗
1ranked-venue papers
1as 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 first-author · 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 |
Hardware accelerators and domain-specific architectures · 91% Energy-efficient computing · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware accelerators and domain-specific architectures
machine learning accelerator |
0.9 | 1 | 2025 | P-DAC: Power-Efficient Photonic Accelerators for LLM Inference · DAC 2025 |
Hardware accelerators and domain-specific architectures
photonic accelerator |
0.9 | 1 | 2025 | P-DAC: Power-Efficient Photonic Accelerators for LLM Inference · DAC 2025 |
Hardware accelerators and domain-specific architectures › machine learning accelerator › transformer accelerator
transformer inference accelerator |
0.9 | 1 | 2025 | P-DAC: Power-Efficient Photonic Accelerators for LLM Inference · DAC 2025 |
Energy-efficient computing
power management |
0.3 | 1 | 2025 | P-DAC: Power-Efficient Photonic Accelerators for LLM Inference · DAC 2025 |
Methods — techniques the papers use, named apart from their topics
optical vector inner product · 0.9digital-to-analog conversion · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | P-DAC: Power-Efficient Photonic Accelerators for LLM InferenceabstractAs traditional electronic hardware encounters the limitations of Moore’s Law, optical computing is emerging as a promising alternative, delivering high data transmission rates, especially beneficial for big data and AI applications. Photonic accelerators, such as the LighteningTransformer, utilize optical analog signals to accelerate Transformerbased models, achieving exceptional speed and low energy consumption. However, controlling modern optical intensity modulators (e.g., MachZehnder Modulators) requires using electrical analog signals (e.g., voltage values) to adjust the optical signal intensity for realizing optical-based vector inner product calculations. Managing this modulation consumes significant power, as it involves selecting optimal electrical values through an electrical controller and converting digital signals to analog using digital-to-analog converters (DACs). In this work, we introduce P-DAC, a solution designed to reduce DAC power consumption, significantly enhancing the energy efficiency of optical accelerators for Transformer models. Wen-Tse Chang, Chun-Feng Wu, Yun-Chen Lo |
DAC | 1 |