Editorial: Efficient AI in particle physics and astrophysics

被引：0

作者：

Duarte, Javier ^{[1
]}

Liu, Mia ^{[2
]}

Ngadiuba, Jennifer ^{[3
]}

Cuoco, Elena ^{[4
,5
]}

Thaler, Jesse ^{[6
,7
]}

机构：

[1] Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA

[2] Purdue Univ, Dept Phys, W Lafayette, IN USA

[3] Fermilab Natl Accelerator Lab, Particle Phys Div, Batavia, IL USA

[4] European Gravitat Observ, Cascina, Italy

[5] Scuola Normale Super Pisa, Pisa, Italy

[6] MIT, Ctr Theoret Phys, Cambridge, MA USA

[7] NSF AI Inst Artificial Intelligence & Fundamental, Cambridge, MA USA

来源：

FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2022年 / 5卷

基金：

美国国家科学基金会;

关键词：

efficiency; FPGA; physics-inspired; neural network; high energy physics; particle astrophysics; pruning; quantization;

D O I：

10.3389/frai.2022.999173

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

引用

页数：3

共 5 条

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[2] CMS Collaboration, 2020, CERNLHCC2020004 CMST
[3] Fast inference of deep neural networks in FPGAs for particle physics
Duarte, J.
Han, S.
Harris, P.
Jindariani, S.
Kreinar, E.
Kreis, B.
Ngadiuba, J.
Pierini, M.
Rivera, R.
Tran, N.
Wu, Z.
[J]. JOURNAL OF INSTRUMENTATION, 2018, 13
[4] Schaub NJ, 2020, Arxiv, DOI arXiv:2006.02909
[5] Sze V., 2020, Synthesis Lectures on Computer Architecture (SLCA), V15, P1