Sadra Rahimi Kari

dblp:407/8121 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Hardware accelerators and domain-specific architectures
machine learning accelerator
0.912025
LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning · ISCA 2025
Interconnection networks and networks-on-chip › optical interconnection networks
optical crossbar
0.912025
LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning · ISCA 2025
Hardware accelerators and domain-specific architectures
photonic accelerator
0.912025
LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning · ISCA 2025
Interconnection networks and networks-on-chip › switch architecture
buffer design
0.312025
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
YearPublicationVenuePosition
2025 LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning
abstract
The 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
ISCA2