A survey on scheduling and mapping techniques in 3D Network-on-chip

Network-on-Chips (NoCs) have been widely employed in the design of multiprocessor system-on-chips (MPSoCs) as a scalable communication solution. NoCs enable communications between on-chip Intellectual Property (IP) cores and allow those cores to achieve higher performance by outsourcing their communication tasks. Mapping and Scheduling methodologies are key elements in assigning application tasks, allocating the tasks to the IPs, and organising communication among them to achieve some specified objectives. The goal of this paper is to present a detailed state-of-the-art of research in the field of mapping and scheduling of applications on 3D NoC, classifying the works based on several dimensions and giving some potential research directions

Energy and performance-aware application mapping for inhomogeneous 3D networks-on-chip

Three dimensional Networks-on-Chip (3D NoCs) have evolved as an ideal solution to the communication demands and complexity of future high density many core architectures. However, the design practicality of 3D NoCs faces several challenges such as thermal issues, high power consumption and area overhead of 3D routers as well as high complexity and cost of vertical link implementation. To mitigate the performance and manufacturing cost of 3D NoCs, inhomogeneous architectures have emerged to combine 2D and 3D routers in 3D NoCs producing lower area and energy consumption while maintaining the performance of homogeneous 3D NoCs. Due to the limited number of vertical links, application mapping on inhomogeneous 3D NoCs can be complex. However, application mapping has a great impact on the performance and energy consumption of NoCs. This paper presents an energy and performance aware application mapping algorithm for inhomogeneous 3D NoCs. The algorithm has been evaluated with various realistic traffic patterns and compared with existing mapping algorithms. Experimental results show NoCs mapped with the proposed algorithm have lower energy consumption and significant reduction in packet delays compared to the existing algorithms and comparable average packet latency with Branch-and-Bound

Evolution of Publications, Subjects, and Co-authorships in Network-On-Chip Research From a Complex Network Perspective

The academia and industry have been pursuing network-on-chip (NoC) related research since two decades ago when there was an urgency to respond to the scaling and technological challenges imposed on intra-chip communication in SoC designs. Like any other research topic, NoC inevitably goes through its life cycle: A. it started up (2000-2007) and quickly gained traction in its own right; B. it then entered the phase of growth and shakeout (2008-2013) with the research outcomes peaked in 2010 and remained high for another four/five years; C. NoC research was considered mature and stable (2014-2020), with signs showing a steady slowdown. Although from time to time, excellent survey articles on different subjects/aspects of NoC appeared in the open literature, yet there is no general consensus on where we are in this NoC roadmap and where we are heading, largely due to lack of an overarching methodology and tool to assess and quantify the research outcomes and evolution. In this paper, we address this issue from the perspective of three specific complex networks, namely the citation network, the subject citation network, and the co-authorship network. The network structure parameters (e.g., modularity, diameter, etc.) and graph dynamics of the three networks are extracted and analyzed, which helps reveal and explain the reasons and the driving forces behind all the changes observed in NoC research over 20 years. Additional analyses are performed in this study to link interesting phenomena surrounding the NoC area. They include: (1) relationships between communities in citation networks and NoC subjects, (2) measure and visualization of a subject\u27s influence score and its evolution, (3) knowledge flow among the six most popular NoC subjects and their relationships, (4) evolution of various subjects in terms of number of publications, (5) collaboration patterns and cross-community collaboration among the authors in NoC research, (6) interesting observation of career lifetime and productivity among NoC researchers, and finally (7) investigation of whether or not new authors are chasing hot subjects in NoC. All these analyses have led to a prediction of publications, subjects, and co-authorship in NoC research in the near future, which is also presented in the paper

Towards Optimal Application Mapping for Energy-Efficient Many-Core Platforms

Siirretty Doriast

Network-on-Chip

Addresses the Challenges Associated with System-on-Chip Integration Network-on-Chip: The Next Generation of System-on-Chip Integration examines the current issues restricting chip-on-chip communication efficiency, and explores Network-on-chip (NoC), a promising alternative that equips designers with the capability to produce a scalable, reusable, and high-performance communication backbone by allowing for the integration of a large number of cores on a single system-on-chip (SoC). This book provides a basic overview of topics associated with NoC-based design: communication infrastructure design, communication methodology, evaluation framework, and mapping of applications onto NoC. It details the design and evaluation of different proposed NoC structures, low-power techniques, signal integrity and reliability issues, application mapping, testing, and future trends. Utilizing examples of chips that have been implemented in industry and academia, this text presents the full architectural design of components verified through implementation in industrial CAD tools. It describes NoC research and developments, incorporates theoretical proofs strengthening the analysis procedures, and includes algorithms used in NoC design and synthesis. In addition, it considers other upcoming NoC issues, such as low-power NoC design, signal integrity issues, NoC testing, reconfiguration, synthesis, and 3-D NoC design. This text comprises 12 chapters and covers: The evolution of NoC from SoC—its research and developmental challenges NoC protocols, elaborating flow control, available network topologies, routing mechanisms, fault tolerance, quality-of-service support, and the design of network interfaces The router design strategies followed in NoCs The evaluation mechanism of NoC architectures The application mapping strategies followed in NoCs Low-power design techniques specifically followed in NoCs The signal integrity and reliability issues of NoC The details of NoC testing strategies reported so far The problem of synthesizing application-specific NoCs Reconfigurable NoC design issues Direction of future research and development in the field of NoC Network-on-Chip: The Next Generation of System-on-Chip Integration covers the basic topics, technology, and future trends relevant to NoC-based design, and can be used by engineers, students, and researchers and other industry professionals interested in computer architecture, embedded systems, and parallel/distributed systems

An investigation of latency prediction for NoC-based communication architectures using machine learning techniques

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Due to the increasing number of cores in Systems on Chip (SoCs), bus architectures have suffered with limitations regarding performance. As applications demand higher bandwidth and lower latencies, buses have not been able to comply with such requirements due to longer wires and increased capacitance. Facing this scenario, Networks on Chip (NoCs) emerged as a way to overcome the limitations found in bus-based systems. Fully exploring all possible NoC characteristics settings is unfeasible due to the vast design space to cover. Therefore, some methods which aim to speed up the design process are needed. In this work, we propose the use of machine learning techniques to optimise NoC architecture components during the design phase. We have investigated the performance of several different ML techniques and selected the Random Forest one targeting audio/video applications. The results have shown an accuracy of up to 90% and 85% for prediction involving arbitration and routing protocols, respectively, and in terms of applications inference, audio/video achieved up to 99%. After this step, we have evaluated other classifiers for each application individually, aiming at finding the adequate one for each situation. The best class of classifiers found was the Tree-based one (Random Forest, Random Tree, and M5P) which is very encouraging, and it points to different approaches from the current state of the art for NoCs latency prediction

Automatic synthesis and optimization of chip multiprocessors

The microprocessor technology has experienced an enormous growth during the last decades. Rapid downscale of the CMOS technology has led to higher operating frequencies and performance densities, facing the fundamental issue of power dissipation. Chip Multiprocessors (CMPs) have become the latest paradigm to improve the power-performance efficiency of computing systems by exploiting the parallelism inherent in applications. Industrial and prototype implementations have already demonstrated the benefits achieved by CMPs with hundreds of cores.CMP architects are challenged to take many complex design decisions. Only a few of them are:- What should be the ratio between the core and cache areas on a chip?- Which core architectures to select?- How many cache levels should the memory subsystem have?- Which interconnect topologies provide efficient on-chip communication?These and many other aspects create a complex multidimensional space for architectural exploration. Design Automation tools become essential to make the architectural exploration feasible under the hard time-to-market constraints. The exploration methods have to be efficient and scalable to handle future generation on-chip architectures with hundreds or thousands of cores.Furthermore, once a CMP has been fabricated, the need for efficient deployment of the many-core processor arises. Intelligent techniques for task mapping and scheduling onto CMPs are necessary to guarantee the full usage of the benefits brought by the many-core technology. These techniques have to consider the peculiarities of the modern architectures, such as availability of enhanced power saving techniques and presence of complex memory hierarchies.This thesis has several objectives. The first objective is to elaborate the methods for efficient analytical modeling and architectural design space exploration of CMPs. The efficiency is achieved by using analytical models instead of simulation, and replacing the exhaustive exploration with an intelligent search strategy. Additionally, these methods incorporate high-level models for physical planning. The related contributions are described in Chapters 3, 4 and 5 of the document.The second objective of this work is to propose a scalable task mapping algorithm onto general-purpose CMPs with power management techniques, for efficient deployment of many-core systems. This contribution is explained in Chapter 6 of this document.Finally, the third objective of this thesis is to address the issues of the on-chip interconnect design and exploration, by developing a model for simultaneous topology customization and deadlock-free routing in Networks-on-Chip. The developed methodology can be applied to various classes of the on-chip systems, ranging from general-purpose chip multiprocessors to application-specific solutions. Chapter 7 describes the proposed model.The presented methods have been thoroughly tested experimentally and the results are described in this dissertation. At the end of the document several possible directions for the future research are proposed

Uncertainty Aware Mapping of Embedded Systems for Reliability, Performance, and Energy

Due to technology downscaling, embedded systems have increased in complexity and heterogeneity. The increasingly large process, voltage, and temperature variations negatively affect the design and optimization process of these systems. These factors contribute to increased uncertainties that in turn undermine the accuracy and effectiveness of traditional design approaches. In this thesis, we formulate the problem of uncertainty aware mapping for multicore embedded system platforms as a multi-objective optimization problem. We present a solution to this problem that integrates uncertainty models as a new design methodology constructed with Monte Carlo and evolutionary algorithms. The solution is uncertainty aware because it is able to model uncertainties in design parameters and to identify robust design points that limit the influence of these uncertainties onto the objective functions. The proposed design methodology is implemented as a tool that can generate the robust Pareto frontier in the objective space formed by reliability, performance, and energy consumption