TY - GEN
T1 - Analyzing Data Intensive Networks on Chips
AU - Zhang, Junwei
AU - Robertazzi, Thomas G.
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A novel framework [2] [3] is proposed to find efficient data-intensive flow distributions on Networks on Chip (NoC). In [3], the authors discussed the virtual cut-through switching, and we extend the flow matrix and analysis in a new switching mechanism, a modified store-and-forward switching mechanism. We explore the various workload distribution applications compared to the previous data load evenly distribution scenario. The new algorithms lead to an efficient makespan and a significant saving in the number of cores used.
AB - A novel framework [2] [3] is proposed to find efficient data-intensive flow distributions on Networks on Chip (NoC). In [3], the authors discussed the virtual cut-through switching, and we extend the flow matrix and analysis in a new switching mechanism, a modified store-and-forward switching mechanism. We explore the various workload distribution applications compared to the previous data load evenly distribution scenario. The new algorithms lead to an efficient makespan and a significant saving in the number of cores used.
KW - Data Intensive Load
KW - Divisible Load Theory
KW - Load Injection
KW - Mesh
KW - Multisource
KW - Network on Chip (NOC)
KW - Voronoi Diagram
UR - https://www.scopus.com/pages/publications/85150678535
U2 - 10.1109/UCC56403.2022.00034
DO - 10.1109/UCC56403.2022.00034
M3 - Conference contribution
T3 - Proceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing, UCC 2022
SP - 185
EP - 186
BT - Proceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing, UCC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2022
Y2 - 6 December 2022 through 9 December 2022
ER -