194 citations
- Intel (United States)US12 papers
- Los Angeles Mission CollegeUS3 papers
- University of California, BerkeleyUS3 papers
- Lawrence Berkeley National LaboratoryUS2 papers
- University of FloridaUS2 papers
- Ames Research CenterUS1 paper
- Arista (United States)US1 paper
- Bay Area Environmental Research InstituteUS1 paper
- Berkeley CollegeUS1 paper
- California Institute of TechnologyUS1 paper
- Centre de Recherche Astrophysique de LyonFR1 paper
- Centre Inria de l'Université Grenoble AlpesFR1 paper
15 papers
Implementation of transformer-based LLMs with large-scale optoelectronic neurons on a CMOS compatible platform
Neil Na, Chih-Hao Cheng, Shou-Chen Hsu +7
The recent rapid deployment of datacenter infrastructures for performing large language models (LLMs) and related artificial intelligence (AI) applications in the clouds is predict…
Design Principles of Dynamic Resource Management for High-Performance Parallel Programming Models
Dominik Huber, Martin Schreiber, Martin Schulz +2
With Dynamic Resource Management (DRM) the resources assigned to a job can be changed dynamically during its execution. From the system's perspective, DRM opens a new level of flex…
Controllable p-type Doping of 2D WSe2 via Vanadium Substitution
Azimkhan Kozhakhmetov, Samuel Stolz, Anne Marie Z. Tan +10
Scalable substitutional doping of two-dimensional (2D) transition metal dichalcogenides (TMDCs) is a prerequisite to developing next-generation logic and memory devices based on 2D…
Fast simulation of quantum algorithms using circuit optimization
Gian Giacomo Guerreschi
Classical simulators play a major role in the development and benchmark of quantum algorithms and practically any software framework for quantum computation provides the option of…
A Substellar Companion to a Hot Star in K2's Campaign 0 Field
S. Dholakia, Ann Marie Cody, Steve B. Howell +6
The K2 mission has enabled searches for transits in crowded stellar environments very different from the original Kepler mission field. We describe here the reduction and analysis…
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Micah J Sheller, G Anthony Reina, Brandon Edwards +2
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labelin…