0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessOnyx is the first fully programmable accelerator for arbitrary sparse tensor algebra kernels. Unlike prior work, it supports higher-order tensors, multiple inpu
Kalhan Koul, Maxwell Strange, Jackson Melchert, Alex Carsello, Yuchen Mei, Olivia Hsu, Taeyoung Kong, Po‐Han Chen, Huifeng Ke, Keyi Zhang, Qiaoyi Liu, Gedeon Nyengele, Akhilesh Balasingam, Jayashree Adivarahan, Ritvik Sharma, Zhouhua Xie, Christopher Torng, Joel Emer, Fredrik Kjølstad, Mark Horowitz, Priyanka Raina (2024). Onyx: A 12nm 756 GOPS/W Coarse-Grained Reconfigurable Array for Accelerating Dense and Sparse Applications. 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), pp. 1-2, DOI: 10.1109/vlsitechnologyandcir46783.2024.10631383.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2024
Authors
21
Datasets
0
Total Files
0
Language
English
Journal
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)
DOI
10.1109/vlsitechnologyandcir46783.2024.10631383
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access
