Randomly Optimized Grid Graph for Low-Latency Interconnection Networks

2016 ◽  
Author(s):  
Koji Nakano ◽  
Daisuke Takafuji ◽  
Satoshi Fujita ◽  
Hiroki Matsutani ◽  
Ikki Fujiwara ◽  
...  
Keyword(s):  
Interconnection Networks ◽  
Low Latency ◽  
Grid Graph

scholarly journals Graph optimization algorithm for low-latency interconnection networks

2021 ◽  
pp. 102805
Author(s):  
Masahiro Nakao ◽  
Maaki Sakai ◽  
Yoshiko Hanada ◽  
Hitoshi Murai ◽  
Mitsuhisa Sato
Keyword(s):  
Optimization Algorithm ◽  
Interconnection Networks ◽  
Low Latency ◽  
Graph Optimization

High-Bandwidth Low-Latency Approximate Interconnection Networks

2017 ◽  
Author(s):  
Daichi Fujiki ◽  
Kiyo Ishii ◽  
Ikki Fujiwara ◽  
Hiroki Matsutani ◽  
Hideharu Amano ◽  
...  
Keyword(s):  
Interconnection Networks ◽  
Low Latency ◽  
High Bandwidth

scholarly journals Toward Sci-φ: A Lightweight Cloud PaaS for Developing Embarrassingly Parallel Applications Based on Jini

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Patrizio Dazzi
Keyword(s):  
Computational Model ◽  
Interconnection Networks ◽  
Efficient Solution ◽  
Large Range ◽  
Parallel Execution ◽  
Parallel Applications ◽  
Low Latency ◽  
Commodity Hardware ◽  
Task Parallel ◽  
High Bandwidth

Embarrassingly parallel problems are characterised by a very small amount of information to be exchanged among the parts they are split in, during their parallel execution. As a consequence they do not require sophisticated, low-latency, high-bandwidth interconnection networks but can be efficiently computed in parallel by exploiting commodity hardware. Basically, this means cheap clusters, networks of workstations and desktops, and Computational Clouds. This computational model can be exploited to compute a quite large range of problems. This paper describes Sci-φ, an almost complete redesign of a previous tool of ours aimed at developing task parallel applications based on Java and Jini that were shown to be an effective and efficient solution in environments like clusters and networks of workstations and desktops.

Small Scale and Low Latency Oriented Neural Network Physical Unclonable Function and its Evaluation

2020 ◽  
Vol 140 (12) ◽  
pp. 1297-1306
Author(s):  
Shu Takemoto ◽  
Kazuya Shibagaki ◽  
Yusuke Nozaki ◽  
Masaya Yoshikawa
Keyword(s):  
Neural Network ◽  
Small Scale ◽  
Low Latency ◽  
Physical Unclonable Function
Sign in / Sign up

Export Citation Format

Share Document