Méthodologies de conception ASIC pour des systèmes sur puce 3D hétérogènes à base de réseaux sur puce 3D

Dans cette thèse, nous étudions les architectures 3D NoC grâce à des implémentations de conception physiques en utilisant la technologie 3D réel mis en oeuvre dans l'industrie. Sur la base des listes d'interconnexions en déroute, nous procédons à l'analyse des performances d'évaluer le bénéfice de l'architecture 3D par rapport à sa mise en oeuvre 2D. Sur la base du flot de conception 3D proposé en se concentrant sur la vérification temporelle tirant parti de l'avantage du retard négligeable de la structure de microbilles pour les connexions verticales, nous avons mené techniques de partitionnement de NoC 3D basé sur l'architecture MPSoC y compris empilement homogène et hétérogène en utilisant Tezzaron 3D IC technlogy. Conception et mise en oeuvre de compromis dans les deux méthodes de partitionnement est étudiée pour avoir un meilleur aperçu sur l'architecture 3D de sorte qu'il peut être exploitée pour des performances optimales. En utilisant l'approche 3D homogène empilage, NoC topologies est explorée afin d'identifier la meilleure topologie entre la topologie 2D et 3D pour la mise en œuvre MPSoC 3D sous l'hypothèse que les chemins critiques est fondée sur les liens inter-routeur. Les explorations architecturales ont également examiné les différentes technologies de traitement. mettant en évidence l'effet de la technologie des procédés à la performance d'architecture 3D en particulier pour l'interconnexion dominant du design. En outre, nous avons effectué hétérogène 3D d'empilage pour la mise en oeuvre MPSoC avec l'approche GALS de style et présenté plusieurs analyses de conception physiques connexes concernant la conception 3D et la mise en œuvre MPSoC utilisant des outils de CAO 2D. Une analyse plus approfondie de l'effet microbilles pas à la performance de l'architecture 3D à l'aide face-à-face d'empilement est également signalé l'identification des problèmes et des limitations à prendre en considération pendant le processus de conception.In this thesis, we study the exploration 3D NoC architectures through physical design implementations using real 3D technology used in the industry. Based on the proposed 3D design flow focusing on timing verification by leveraging the benefit of negligible delay of microbumps structure for vertical connections, we have conducted partitioning techniques for 3D NoC-based MPSoC architecture including homogeneous and heterogeneous stacking using Tezzaron 3D IC technlogy. Design and implementation trade-off in both partitioning methods is investigated to have better insight about 3D architecture so that it can be exploited for optimal performance. Using homogeneous 3D stacking approach, NoC architectures are explored to identify the best topology between 2D and 3D topology for 3D MPSoC implementation. The architectural explorations have also considered different process technologies highlighting the wire delay effect to the 3D architecture performance especially for interconnect-dominated design. Additionally, we performed heterogeneous 3D stacking of NoC-based MPSoC implementation with GALS style approach and presented several physical designs related analyses regarding 3D MPSoC design and implementation using 2D EDA tools. Finally we conducted an exploration of 2D EDA tool on different 3D architecture to evaluate the impact of 2D EDA tools on the 3D architecture performance. Since there is no commercialize 3D design tool until now, the experiment is important on the basis that designing 3D architecture using 2D EDA tools does not have a strong and direct impact to the 3D architecture performance mainly because the tools is dedicated for 2D architecture design.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Analyse et caractérisation des couplages substrat et de la connectique dans les circuits 3D : Vers des modèles compacts

The 3D integration is the most promising technological solution to track the level of integration dictated by Moore's Law (see more than Moore, Moore versus more). It leads to important research for a dozen years. It can superimpose different circuits and components in one box. Its main advantage is to allow a combination of heterogeneous and highly specialized technologies for the establishment of a complete system, while maintaining a high level of performance with very short connections between the different circuits. The objective of this work is to provide consistent modeling via crossing, and / or contacts in the substrate, with various degrees of finesse / precision to allow the high-level designer to manage and especially to optimize the partitioning between the different strata. This modelization involves the development of multiple views at different levels of abstraction: the physical model to "high level" model. This would allow to address various issues faced in the design process: - The physical model using an electromagnetic simulation based on 2D or 3D ( finite element solver ) is used to optimize the via (materials, dimensions etc..) It determines the electrical performance of the via, including high frequency. Electromagnetic simulations also quantify the coupling between adjacent via. - The analytical compact of via their coupling model, based on a description of transmission line or Green cores is used for the simulations at the block level and Spice type simulations. Analytical models are often validated against measurements and / or physical models.L’intégration 3D est la solution technologique la plus prometteuse pour suivre le niveau d’intégration dictée par la loi de Moore (cf. more than Moore, versus more Moore). Elle entraine des travaux de recherche importants depuis une douzaine d’années. Elle permet de superposer différents circuits et composants dans un seul boitier. Son principal avantage est de permettre une association de technologies hétérogènes et très spécialisées pour la constitution d’un système complet, tout en préservant un très haut niveau de performance grâce à des connexions très courtes entre ces différents circuits. L’objectif de ce travail est de fournir des modélisations cohérentes de via traversant, ou/et de contacts dans le substrat, avec plusieurs degrés de finesse/précision, pour permettre au concepteur de haut niveau de gérer et surtout d’optimiser le partitionnement entre les différentes strates. Cette modélisation passe par le développement de plusieurs vues à différents niveaux d’abstraction: du modèle physique au modèle « haut niveau ». Elle devait permettre de répondre à différentes questions rencontrées dans le processus de conception :- le modèle physique de via basé sur une simulation électromagnétique 2D ou 3D (solveur « éléments finis ») est utilisé pour optimiser l’architecture du via (matériaux, dimensions etc.) Il permet de déterminer les performances électriques des via, notamment en haute fréquence. Les simulations électromagnétiques permettent également de quantifier le couplage entre via adjacents. - le modèle compact analytique de via et de leur couplage, basé sur une description de type ligne de transmission ou noyaux de Green, est utilisé pour les simulations au niveau bloc, ainsi que des simulations de type Spice. Les modèles analytiques sont souvent validés par rapport à des mesures et/ou des modèles physiques

Architecture and Advanced Electronics Pathways Toward Highly Adaptive Energy- Efficient Computing

With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management

A Modern Primer on Processing in Memory

Modern computing systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in computing that cause performance, scalability and energy bottlenecks: (1) data access is a key bottleneck as many important applications are increasingly data-intensive, and memory bandwidth and energy do not scale well, (2) energy consumption is a key limiter in almost all computing platforms, especially server and mobile systems, (3) data movement, especially off-chip to on-chip, is very expensive in terms of bandwidth, energy and latency, much more so than computation. These trends are especially severely-felt in the data-intensive server and energy-constrained mobile systems of today. At the same time, conventional memory technology is facing many technology scaling challenges in terms of reliability, energy, and performance. As a result, memory system architects are open to organizing memory in different ways and making it more intelligent, at the expense of higher cost. The emergence of 3D-stacked memory plus logic, the adoption of error correcting codes inside the latest DRAM chips, proliferation of different main memory standards and chips, specialized for different purposes (e.g., graphics, low-power, high bandwidth, low latency), and the necessity of designing new solutions to serious reliability and security issues, such as the RowHammer phenomenon, are an evidence of this trend. This chapter discusses recent research that aims to practically enable computation close to data, an approach we call processing-in-memory (PIM). PIM places computation mechanisms in or near where the data is stored (i.e., inside the memory chips, in the logic layer of 3D-stacked memory, or in the memory controllers), so that data movement between the computation units and memory is reduced or eliminated.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0398

High-Density Solid-State Memory Devices and Technologies

This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms

Supervised Design-Space Exploration

Low-cost Very Large Scale Integration (VLSI) electronics have revolutionized daily life and expanded the role of computation in science and engineering. Meanwhile, process-technology scaling has changed VLSI design to an exploration process that strives for the optimal balance among multiple objectives, such as power, performance, and area, i.e. multi-objective Pareto-set optimization. Besides, modern VLSI design has shifted to synthesis-centric methodologies in order to boost the design productivity, which leads to better design quality given limited time and resources. However, current decade-old synthesis-centric design methodologies suffer from: (i) long synthesis tool runtime, (ii) elusive optimal setting of many synthesis knobs, (iii) limitation to one design implementation per synthesis run, and (iv) limited capability of digesting only component-level designs as opposed to holistic system-wide synthesis. These challenges make Design Space Exploration (DSE) with synthesis tools a daunting task for both novice and experienced VLSI designers, thus stagnating the development of more powerful (i.e. more complex) computer systems. To address these challenges, I propose Supervised Design-Space Exploration (SDSE), an abstraction layer between a designer and a synthesis tool, aiming to autonomously supervise synthesis jobs for DSE. For system-level exploration, SDSE can approximate a system Pareto set given limited information: only lightweight component characterization is required, yet the necessary component synthesis jobs are discovered on-the-fly in order to compose the system Pareto set. For component-level exploration, SDSE can approximate a component Pareto set by iteratively refining the approximation with promising knob settings, guided by synthesis-result estimation with machine-learning models. Combined, SDSE has been applied with the three major synthesis stages, namely high-level, logic, and physical synthesis, to the design of heterogeneous accelerator cores as well as high-performance processor cores. In particular, SDSE has been successfully integrated into the IBM Synthesis Tuning System, yielding 20% better circuit performance than the original system on the design of a 22nm server processor that is currently in production. Looking ahead, SDSE can be applied to other VLSI designs beyond the accelerator and the programmable cores. Moreover, SDSE opens several research avenues for: (i) new development and deployment platforms of synthesis tools, (ii) large-scale collaborative design engineering, and (iii) new computer-aided design approaches for new classes of systems beyond VLSI chips

Three-Dimensional Processing-In-Memory-Architectures: A Holistic Tool For Modeling And Simulation

Die gemeinhin als Memory Wall bekannte, sich stetig weitende Leistungslücke zwischen Prozessor- und Speicherarchitekturen erfordert neue Konzepte, um weiterhin eine Skalierung der Rechenleistung zu ermöglichen. Da Speicher als die Beschränkung innerhalb einer Von-Neumann-Architektur identifiziert wurden, widmet sich die Arbeit dieser Problemstellung. Obgleich dreidimensionale Speicher zu einer Linderung der Memory Wall beitragen können, sind diese alleinig für die zukünftige Skalierung ungenügend. Aufgrund höherer Effizienzen stellt die Integration von Rechenkapazität in den Speicher (Processing-In-Memory, PIM) ein vielversprechender Ausweg dar, jedoch existiert ein Mangel an PIM-Simulationsmodellen. Daher wurde ein flexibles Simulationswerkzeug für dreidimensionale Speicherstapel geschaffen, welches zur Modellierung von dreidimensionalen PIM erweitert wurde. Dieses kann Speicherstapel wie etwa Hybrid Memory Cube standardkonform simulieren und bietet zugleich eine hohe Genauigkeit indem auf elementaren Datenpaketen in Kombination mit dem Hardware validierten Simulator BOBSim modelliert wird. Ein eigens entworfener Simulationstaktbaum ermöglicht zugleich eine schnelle Ausführung. Messungen weisen im funktionalen Modus eine 100-fache Beschleunigung auf, wohingegen eine Verdoppelung der Ausführungsgeschwindigkeit mit Taktgenauigkeit erzielt wird. Anhand eines eigens implementierten, binärkompatiblen GPU-Beschleunigers wird die Modellierung einer vollständig dreidimensionalen PIM-Architektur demonstriert. Dabei orientieren sich die maximalen Hardwareressourcen an einem PIM-Beschleuniger aus der Literatur. Evaluiert wird einerseits das GPU-Simulationsmodell eigenständig, andererseits als PIM-Verbund jeweils mit Hilfe einer repräsentativ gewählten, speicherbeschränkten geophysikalischen Bildverarbeitung. Bei alleiniger Betrachtung des GPU-Simulationsmodells weist dieses eine signifikant gesteigerte Simulationsgeschwindigkeit auf, bei gleichzeitiger Abweichung von 6% gegenüber dem Verilator-Modell. Nachfolgend werden innerhalb dieser Arbeit unterschiedliche Konfigurationen des integrierten PIM-Beschleunigers evaluiert. Je nach gewählter Konfiguration kann der genutzte Algorithmus entweder bis zu 140GFLOPS an tatsächlicher Rechenleistung abrufen oder eine maximale Recheneffizienz von synthetisch 30% bzw. real 24,5% erzielen. Letzteres stellt eine Verdopplung des Stands der Technik dar. Eine anknüpfende Diskussion erläutert eingehend die Resultate.The steadily widening performance gap between processor- and memory-architectures - commonly known as the Memory Wall - requires novel concepts to achieve further scaling in processing performance. As memories were identified as the limitation within a Von-Neumann-architecture, this work addresses this constraining issue. Although three-dimensional memories alleviate the effects of the Memory Wall, the sole utilization of such memories would be insufficient. Due to higher efficiencies, the integration of processing capacity into memories (so-called Processing-In-Memory, PIM) depicts a promising alternative. However, a lack of PIM simulation models still remains. As a consequence, a flexible simulation tool for three-dimensional stacked memories was established, which was extended for modeling three-dimensional PIM architectures. This tool can simulate stacked memories such as Hybrid Memory Cube standard-compliant and simultaneously offers high accuracy by modeling on elementary data packets (FLIT) in combination with the hardware validated BOBSim simulator. To this, a specifically designed simulation clock tree enables an rapid simulation execution. A 100x speed up in simulation execution can be measured while utilizing the functional mode, whereas a 2x speed up is achieved during clock-cycle accuracy mode. With the aid of a specifically implemented, binary compatible GPU accelerator and the established tool, the modeling of a holistic three-dimensional PIM architecture is demonstrated within this work. Hardware resources used were constrained by a PIM architecture from literature. A representative, memory-bound, geophysical imaging algorithm was leveraged to evaluate the GPU model as well as the compound PIM simulation model. The sole GPU simulation model depicts a significantly improved simulation performance with a deviation of 6% compared to a Verilator model. Subsequently, various PIM accelerator configurations with the integrated GPU model were evaluated. Depending on the chosen PIM configuration, the utilized algorithm achieves 140GFLOPS of processing performance or a maximum computing efficiency of synthetically 30% or realistically 24.5%. The latter depicts a 2x improvement compared to state-of-the-art. A following discussion showcases the results in depth