Partial Bus-Invert Coding for Power Optimization of Application-Specific Systems

This paper presents two bus coding schemes for power optimization of application-specific systems: Partial Bus-Invert coding and its extension to Multiway Partial Bus-Invert coding. In the first scheme, only a selected subgroup of bus lines is encoded to avoid unnecessary inversion of relatively inactive and/or uncorrelated bus lines which are not included in the subgroup. In the extended scheme, we partition a bus into multiple subbuses by clustering highly correlated bus lines and then encode each subbus independently. We describe a heuristic algorithm of partitioning a bus into subbuses for each encoding scheme. Experimental results for various examples indicate that both encoding schemes are highly efficient for application-specific systems

Méthodologie de génération de plateforme de prototypage à base de multi-fpga

Multi-FPGA based prototyping is no longer optional for hardware/software integration. We can classify multi-FPGA prototyping platforms in three categories: off-the-shelf, custom and cabling. The cabling platform is semi off-the-shelf and semi custom. Nevertheless, crafting a custom and a cabling platform is today a manual process, which is time-consuming. The performance and the cost of the platform lie on the FPGA expertise and SoC DUT knowledge of the engineers. Compared to OTS platforms, the added value, in terms of performance, of cabling or custom platforms can be heavily impaired by an inefficient board design. Moreover, FPGA I/Os are becoming a scarce resource, worsening the inter-FPGA bandwidth generation after generation. Therefore, it becomes more and more difficult to prototype an SoC/ASIC design at proper performance. The contributions of the manuscript are: (1). An automatic implementation flow for an OTS platform is proposed. (2). An automatic design flow for creating a custom platform is proposed, thus increasing the productivity, enabling the board exploration, and optimizing cost and performance. (3). The cabling platform is proposed where one board is composed of one FPGA and several connectors, with an algorithm to automatically find a solution for the cable distribution. (4). Thanks to the developed automatic tools, the three different multi-FPGA platforms are compared. The custom platform always achieves better performance and lower deployment cost, but still with 3-5 months in time of availability. If the performance or the deployment cost are not rigorous constraints, the cabling platform offers an attractive alternative compared to others.Face à la difficulté de l’intégration matériel/logiciel, le prototypage à base de multi-FPGA devient obligatoire dans la vérification pré-silicium. Les plateformes de prototypage peuvent être classées en trois catégories: OTS, sur mesure et câblées. La plateforme câblée est semi OTS et semi sur mesure. Néanmoins, la création d’une plateforme sur mesure et câblée est un processus manuel et chronophage. La performance et le coût de la plateforme dépend de l'expérience de concepteurs en expertise de FPGA et connaissance du système sur puce. Par rapport à des plateformes OTS, la valeur ajoutée, en terme de performance, des plateformes câblées ou sur mesure peuvent être fortement dégradée par une carte inefficace. En plus, FPGA E/S devient une ressource rare, aggravant la bande passante inter-FPGA. Par conséquent, il devient de plus en plus difficile de prototyper un design à une performance satisfaisante. Les contributions sont: (1). Un flot de implémentation automatique pour une plateforme OTS. (2). Un flot de conception automatique pour créer une plateforme sur mesure, ainsi augmentant la productivité, permettant l’exploration de carte et optimisant le coût et la performance. (3). La plateforme câblée avec un algorithme permettant automatiquement de trouver une solution pour la distribution des câbles. (4). Grâce aux flots automatique, les trois plateformes sont comparées. La plateforme sur mesure toujours réalise plus de performance et moins de coût de déploiement, mais encore avec 3-5 mois en temps de disponibilité. Si la performance ou le coût de déploiement ne sont pas les contraintes strictes, la plateforme câblée est une alternative intéressante par rapport aux autres

Doctor of Philosophy

dissertationRecent breakthroughs in silicon photonics technology are enabling the integration of optical devices into silicon-based semiconductor processes. Photonics technology enables high-speed, high-bandwidth, and high-fidelity communications on the chip-scale-an important development in an increasingly communications-oriented semiconductor world. Significant developments in silicon photonic manufacturing and integration are also enabling investigations into applications beyond that of traditional telecom: sensing, filtering, signal processing, quantum technology-and even optical computing. In effect, we are now seeing a convergence of communications and computation, where the traditional roles of optics and microelectronics are becoming blurred. As the applications for opto-electronic integrated circuits (OEICs) are developed, and manufacturing capabilities expand, design support is necessary to fully exploit the potential of this optics technology. Such design support for moving beyond custom-design to automated synthesis and optimization is not well developed. Scalability requires abstractions, which in turn enables and requires the use of optimization algorithms and design methodology flows. Design automation represents an opportunity to take OEIC design to a larger scale, facilitating design-space exploration, and laying the foundation for current and future optical applications-thus fully realizing the potential of this technology. This dissertation proposes design automation for integrated optic system design. Using a buildingblock model for optical devices, we provide an EDA-inspired design flow and methodologies for optical design automation. Underlying these flows and methodologies are new supporting techniques in behavioral and physical synthesis, as well as device-resynthesis techniques for thermal-aware system integration. We also provide modeling for optical devices and determine optimization and constraint parameters that guide the automation techniques. Our techniques and methodologies are then applied to the design and optimization of optical circuits and devices. Experimental results are analyzed to evaluate their efficacy. We conclude with discussions on the contributions and limitations of the approaches in the context of optical design automation, and describe the tremendous opportunities for future research in design automation for integrated optics

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

On Discrete Hyperbox Packing

Bin packing is a very important and popular research area in the computer science field. Past work showed many good and real-world packing algorithms. How- ever, due to the complexity of the problem in multiple-dimensional bin packing, also called hyperbox packing, we need more practical packing algorithms for its real-world applications. In this dissertation, we extend 1D packing algorithms to hyperbox packing prob- lems via a general framework that takes two inputs of a 1D packing algorithm and an instance of hyperbox packing problem and outputs a hyperbox packing algorithm. The extension framework significantly enriches the family of hyperbox-packing algorithms, generates many framework-based algorithms, and simultaneously calls for the analysis for those algorithms. We also analyze the performance of a couple of framework-based algorithms from two perspectives of worst-case performance and average-case performance. In worst- case analysis, we use the worst-case performance ratio as our metric and analyze the relationship of the ratio of framework-based algorithms and that of the corresponding 1D algorithms. We also compare their worst-case performance against two baselines: strip optimal algorithms and optimal algorithms. In average-case analysis, we use expected waste as a metric, analyze the waste of optimal hyperbox packing algorithms, and estimate the asymptotic forms of the waste for framework-based algorithms

Multiterminal Net Routing For Partial Crossbar-Based Multi-Fpga Systems

Multi-FPGA (field-programmable gate arrays) systems are used as custom computing machines to solve compute-intensive problems and also in the verification and prototyping of large circuits. In this paper, we address the problem of routing multiterminal nets in a multi-FPGA system that uses partial crossbars as interconnect structures. First, we model the multiterminal routing problem as a partitioned bin-packing problem and formulate it as an integer linear programming problem where the number of variables is exponential. A fast heuristic is applied to compute an upper bound on the routing solution. Then, a column generation technique is used to solve the linear relaxation of the initial master problem in order to obtain a lower bound on the routing solution. This is followed by an iterative branch-and-price procedure that attempts to find a routing solution somewhere between the two established bounds. In this regard, the proposed algorithm guarantees an exact-routing solution by searching a branch-and-price tree. Due to the tightness of the bounds, the branch-and-price tree is small resulting in shorter execution times. Experimental results are provided for different netlists and board configurations in order to demonstrate the algorithms performance. The obtained results show that the algorithm finds an exact routing solution in a very short time

Multiterminal net routing for partial crossbar-based multi-FPGA systems

Multi-FPGA (field-programmable gate arrays) systems are used as custom computing machines to solve compute-intensive problems and also in the verification and prototyping of large circuits. In this paper, we address the problem of routing multiterminal nets in a multi-FPGA system that uses partial crossbars as interconnect structures. First, we model the multiterminal routing problem as a partitioned bin-packing problem and formulate it as an integer linear programming problem where the number of variables is exponential. A fast heuristic is applied to compute an upper bound on the routing solution. Then, a column generation technique is used to solve the linear relaxation of the initial master problem in order to obtain a lower bound on the routing solution. This is followed by an iterative branch-and-price procedure that attempts to find a routing solution somewhere between the two established bounds. In this regard, the proposed algorithm guarantees an exact-routing solution by searching a branch-and-price tree. Due to the tightness of the bounds, the branch-and-price tree is small resulting in shorter execution times. Experimental results are provided for different netlists and board configurations in order to demonstrate the algorithms performance. The obtained results show that the algorithm finds an exact routing solution in a very short time