Search

Design flaws and unseen vulnerabilities in the photonic network-on-chip (PNoC) have made it a focal point of potential security attacks. Previous works well investigate remote attacks launched outside from electrical NoCs. However, it is still a challenge to find possible threats and efficient countermeasures for PNoCs. In PNoCs, for example, micro resonators can be maliciously controlled by a remote attacker to miss expected tuning function, thus leading to data loss and information disclosure. In this article, we first summarize remote denial of service attacks caused by hardware Trojan insertion and timing channel in NoCs. We then show that these two kinds of remote attacks are also effective threat models in PNoCs. We design, for the first time, countermeasures protecting against threat models in the hybrid wavelength and mode division multiplexing PNoC. Finally, we propose the challenging issues of enhancing the safety of the 3D PNoC under new potential threats. [ABSTRACT FROM AUTHOR]

In sharing economy, people offer idle social resources to others in a sharing manner. Through community-based online platforms, the people offering services can earn commission while others can enjoy a better life via renting social resources. Consequently, the value-in-use of services is expectedly strengthened within the unit time, although the total amount of social resources remains constant. Influenced by sharing economy, some famous companies have developed intelligent systems to analyze the most appropriate coincidence between citizens’ idle supply and renting demand from numerous data sets. However, the big data analysis of the optimal service-demand matching usually runs on the traditional multiprocessors equipped in intelligent systems, so-called “system-on-chip.” In this paper, we design a novel computer architecture—the accelerator based on optical network-on-chip (ONoC)—to further speed up the matching between citizens’ offer and demand in sharing economy. Our ONoC-based accelerator is able to quickly calculate the optimal service-demand matching by processing computation tasks on parallel cores, i.e., task-core mapping. In addition, to improve the accelerator reliability, the assorted task-core mapping algorithm is also designed. The extensive simulation results based on real trace file demonstrate the effectiveness of our system and algorithm. Note to Practitioners—Sharing economy is of great importance for realizing green consumption and sustainable development in our human society. Sharing economy enterprise calls for intelligent system design for service-demand matching in the current big data era. In this paper, we design the accelerator based on ONoC to further speed up the matching between citizens’ offer and demand in sharing economy. By processing computation tasks on parallel cores using our algorithm, the task-core mapping can be performed with high speed and reliability. The simulation results—based on the trace file of Amazon Mechanical Turk—can well guide the practitioners to design a more clever and reliable product by quickly calculating the optimal service-demand matching. [ABSTRACT FROM AUTHOR]