VLDB 2026 Research / reviewers in the wild / expert
Sadra Rahimi Kari
dblp:407/8121
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0001-8230-2165ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 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 |
Hardware accelerators and domain-specific architectures · 61% Interconnection networks and networks-on-chip · 39% |
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 | LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning · ISCA 2025 |
Interconnection networks and networks-on-chip › optical interconnection networks
optical crossbar |
0.9 | 1 | 2025 | LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning · ISCA 2025 |
Hardware accelerators and domain-specific architectures
photonic accelerator |
0.9 | 1 | 2025 | LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning · ISCA 2025 |
Interconnection networks and networks-on-chip › switch architecture
buffer design |
0.3 | 1 | 2025 | LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning · ISCA 2025 |
Methods — techniques the papers use, named apart from their topics
matrix multiplication · 0.9convolutional layers · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | LightML: A Photonic Accelerator for Efficient General Purpose Machine LearningabstractThe rapid integration of AI technologies into everyday life across sectors such as healthcare, autonomous driving, and smart home applications requires extensive computational resources, placing strain on server infrastructure and incurring significant costs.We present LightML, the first system-level photonic crossbar design, optimized for high-performance machine learning applications.This work provides the first complete memory and buffer architecture carefully designed to support the high-speed photonic crossbar, achieving over 80% utilization.LightML also introduces solutions for key ML functions, including large-scale matrix multiplication (MMM), element-wise operations, non-linear functions, and convolutional layers.Delivering 325 TOP/s at only 3 watts, LightML offers significant improvements in speed and power efficiency, making it ideal for both edge devices and dense data center workloads. Sadra Rahimi Kari, Xin Xin 0008, Nathan Youngblood, Youtao Zhang, Jun Yang 0002 |
ISCA | 2 |