TY - GEN
T1 - StocNoC
T2 - 16th International Symposium on Applied Reconfigurable Computing, ARC 2020
AU - Zhanbolatov, Arshyn
AU - Vipin, Kizheppatt
AU - Dadlani, Aresh
AU - Fedorov, Dmitriy
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Spreading dynamics of many real-world processes lean heavily on the topological characteristics of the underlying contact network. With the rapid temporal and spatial evolution of complex inter-connected networks, microscopic modeling and stochastic simulation of individual-based interactions have become challenging in both, time and state space. Driven by the surge to reduce the time complexity associated with system behavior analysis over different network structures, we propose a network-on-chip (NoC) based FPGA solution called StocNoC. The proof of concept is supported by the design, implementation and evaluation of the classical heterogeneous susceptible-infected-susceptible (SIS) epidemic model on a scalable NoC. The steady-state results from the proposed implementation for the fractions of susceptible and infected nodes are shown to be comparable to those acquired from software simulations, but in a significantly shorter time period. Analogous to network information diffusion, implementation of the SIS model and its variants will be beneficial to foresee possible epidemic outbreaks earlier in time and expedite control decisions.
AB - Spreading dynamics of many real-world processes lean heavily on the topological characteristics of the underlying contact network. With the rapid temporal and spatial evolution of complex inter-connected networks, microscopic modeling and stochastic simulation of individual-based interactions have become challenging in both, time and state space. Driven by the surge to reduce the time complexity associated with system behavior analysis over different network structures, we propose a network-on-chip (NoC) based FPGA solution called StocNoC. The proof of concept is supported by the design, implementation and evaluation of the classical heterogeneous susceptible-infected-susceptible (SIS) epidemic model on a scalable NoC. The steady-state results from the proposed implementation for the fractions of susceptible and infected nodes are shown to be comparable to those acquired from software simulations, but in a significantly shorter time period. Analogous to network information diffusion, implementation of the SIS model and its variants will be beneficial to foresee possible epidemic outbreaks earlier in time and expedite control decisions.
UR - http://www.scopus.com/inward/record.url?scp=85083031375&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083031375&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-44534-8_27
DO - 10.1007/978-3-030-44534-8_27
M3 - Conference contribution
AN - SCOPUS:85083031375
SN - 9783030445331
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 361
EP - 375
BT - Applied Reconfigurable Computing. Architectures, Tools, and Applications - 16th International Symposium, ARC 2020, Proceedings
A2 - Rincón, Fernando
A2 - Barba, Jesús
A2 - Caba, Julián
A2 - So, Hayden K.H.
A2 - Diniz, Pedro
PB - Springer
Y2 - 1 April 2020 through 3 April 2020
ER -