Architecture de type Harvard | EPFL Graph Search

L’architecture de type Harvard est une conception des processeurs qui sépare physiquement la mémoire de données et la mémoire programme. L’accès à chacune des deux mémoires s’effectue via deux bus distincts. Historique Le nom de cette structure vient du nom de l’université Harvard où une telle architecture a été mise en pratique pour la première fois avec le Mark I en 1944. Caractéristiques Avec deux bus distincts, l’architecture dite de Harvard permet de transférer simultanément les données et les instructions à exécuter. Ainsi, l’unité de traitement aura accès simultanément à l’instruction et aux données associées. Cette architecture peut se montrer plus rapide à technologie identique que l'architecture de von Neumann ; le gain en performance s'obtient cependant au prix d'une complexité accrue de structure. Autres architectures L’architecture de type von Neumann s’oppose à celle de Harvard car elle utilise une unique structure pour stocker à la

Establishing a base of trust with performance counters for enterprise workloads

Andrzej Pawel Nowak, Willy Zwaenepoel

Understanding the performance of large, complex enterprise-class applications is an important, yet nontrivial task. Methods using hardware performance counters, such as profiling through event-based sampling, are often favored over instrumentation for analyzing such large codes, but rarely provide good accuracy at the instruction level. This work evaluates the accuracy ofmultiple eventbased sampling techniques and quantifies the impact of a range of improvements suggested in recent years. The evaluation is performed on instances of three modern CPU architectures, using designated kernels and full applications. We conclude that precisely distributed events considerably improve accuracy, with further improvements possible when using Last Branch Records. We also present practical recommendations for hardware architects, tool developers and performance engineers, aimed at improving the quality of results.

2015

Amorphous membrane blending

Christof Teuscher

The von Neumann architecture was first expressed in 1945 and has largely dominated in many variants and refinements computer science for more than half a century. Alternative architectures always occupied a marginal place only, despite a growing need for new concepts and paradigms in computer science. Biologically-inspired engineering applies biological concepts to the design of novel computing machines and algorithms. This can lead to the creation of new machines, endowed with properties usually associated with the living world: adaptation, evolution, growth and development, fault-tolerance, self-replication or cloning, reproduction, etc. Most of these approaches are based on well established theories such as artificial neural networks, evolutionary algorithms, and cellular automata. The work presented in this thesis takes an alternative path and proposes concepts for novel and unconventional biologically-inspired machines. The approach is mainly motivated by the insight that tomorrow's computational substrates and environments might be very different from what we know today. Some of tomorrow's computers might be embedded in the paint that covers your desk or printed on a sheet of paper by means of a special ink. Most of such pervasive computing concepts have some common elements: (1) the computer's basic elements are very simple, identical, and available in a huge number, (2) the interactions between the elements are purely local, (3) the elements as well as the interconnections are unreliable, and (4) there is no global control mechanism available. This thesis is mainly based on the unification of the following three domains of research: (1) amorphous computing, (2) membrane systems, and (3) blending. An amorphous computer is a massive parallel machine made up of myriads of simple, unreliable, and identical elements, distributed randomly on a surface and interconnected locally by unreliable connections. Membrane systems are theoretical models inspired by biochemistry-based on regions bounded by membranes. The hierarchical membrane structures contain artificial chemistries, consisting in objects and reactions, which allow to do computations. Blending is a framework of cognitive science which tries to explain how we deal with mental concepts and how creative thinking emerges. First, an introduction of traditional bio-inspired machines and hardware is provided. This part also includes the presentation of a first implementation of a membrane system on reconfigurable hardware and a description of the cellular automata machine entitled BioWall, with its applications. Random boolean networks as well as several theoretical considerations and practical results are then used to introduce irregular computational structures. The C-Blending approach represents an novel computational blending method intended for membrane systems and artificial chemistries. In order to implement membrane systems on amorphous computers, the Circuit Amorphous Computer as well as special membrane systems, termed aP and aB membrane systems, are proposed. The ultimate concept proposed and studied consists in a unification of membrane systems, amorphous computers, and computational C-Blending. The unification of the three concepts results in several interesting properties. The cellular structures allow to create dynamical hierarchies and growing systems whereas the artificial chemistries represent an ideal mean to compute on the potentially imperfect and irregular hardware of an amorphous computer. Finally, the computational blending proposed describes an inventive method to create, organize, and adapt membrane systems. The characteristics and limits of the concepts proposed are analyzed and validated using various examples and toy applications. The thesis concludes with the definition of the Circuit Amorphous Computer and the Amorphon architecture, which might constitute the minimal element of tomorrow's computing machines.

EPFL2004

Biosensors for Bimolecular Computing: a Review and Future Perspectives

Simone Aiassa, Sandro Carrara, Danilo Demarchi

Biomolecular computing is the field of engineering where computation, storage, communication, and coding are obtained by exploiting interactions between biomolecules, especially DNA, RNA, and enzymes. They are a promising solution in a long-term vision, bringing huge parallelism and negligible power consumption. Despite significant efforts in taking advantage of the massive computational power of biomolecules, many issues are still open along the way for considering biomolecular circuits as an alternative or a complement to competing with complementary metal-oxide-semiconductor (CMOS) architectures. According to the Von Neumann architecture, computing systems are composed of a central processing unit, a storage unit, and input and output (I/O). I/O operations are crucial to drive and read the computing core and to interface it to other devices. In emerging technologies, the complexity overhead and the bottleneck of I/O systems are usually limiting factors. While computing units and memories based on biomolecular systems have been successfully presented in literature, the published I/O operations are still based on laboratory equipment without a real development of integrated I/O. Biosensors are suitable devices for transducing biomolecular interactions by converting them into electrical signals. In this work, we explore the latest advancements in biomolecular computing, as well as in biosensors, with focus on technology suitable to provide the required and still missing I/O devices. Therefore, our goal is to picture out the present and future perspectives about DNA, RNA, and enzymatic-based computing according to the progression in its I/O technologies, and to understand how the field of biosensors contributes to the research beyond CMOS.

SPRINGER2020

Amorphous membrane blending

Christof Teuscher

EPFL2004