Network-on-chip (NoC) is evolving as a better substitute for incorporating a large number of cores on a single system-on-chip (SoC). The dependency on multi-core systems to accomplish the highperformance constraints of composite embedded applications is on the rise. This leads to the realization of efficient mapping approaches for such complex applications. The significance of efficient application mapping approaches has increased ever since the embedded applications have become more complex and performance-oriented. This paper presents the detailed comparative analysis and categorization of application mapping approaches with current trends in NoC design implementation. These approaches target to improve the performance of the whole system by optimizing communication cost, energy, power consumption, and latency. Apart from the categorization of the discussed approaches, comparison of communication cost, power, energy, and latency of the NoC system carried out on real applications like VOPD and MPEG4. Moreover, the best technique identified in each category based on the evaluation of performance results. INDEX TERMS Network-on-Chip, application mapping, NoC design, VOPD, System-on-Chip. I. INTRODUCTION System-on-Chip (SoC) is an archetype for the design and implementation of on-chip circuits that can support multiple systems on a single chip. The ever-rising number of processing cores on a single chip has made the efficiency of onchip designs as one of the major aspects in evaluating the average performance of embedded SoC. To fulfill the performance needs and to provide flexibility in the designs the field of Network on Chip (NoC) has emerged that separates the communication from the computation. Many surveys [1]-[7] are published in general and textbooks are also available on the topic [8]-[10]. There is still a need to solve more advanced research problems in NoC. Application mapping on NoC architecture has a prominent place among all the research problems which can be arbitrated from the published The associate editor coordinating the review of this manuscript and approving it for publication was Eyuphan Bulut. papers and current trends. This survey assumes a textbooklevel acquaintance of NoC terminology. The aim is to provide a comprehensive overview of all the aspects of application mapping (task graph generation, scheduling, optimization techniques, simulation setup, and performance evaluation metrics). The organization of this survey is as follows; first, we provide the reader the need and overview of application mapping in NoC presented in Section I. A detailed review and calcification of application mapping techniques in NoCs are discussed in Section II. The current and latest trends in application mapping are summarized in Section III. In Section IV performance comparison and the points that can create the difference between these techniques are highlighted. Finally, Section VI concludes this paper. A. MOTIVATION AND SCOPE Mostly synthetic traffic patterns are used to imitate the functionalities of rea...
The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.
Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an N P-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, Manuscript
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