Speeding-up model-based fault injection of deep-submicron CMOS fault models through dynamic and partially reconfigurable FPGAS

Actualmente, las tecnologías CMOS submicrónicas son básicas para el desarrollo de los modernos sistemas basados en computadores, cuyo uso simplifica enormemente nuestra vida diaria en una gran variedad de entornos, como el gobierno, comercio y banca electrónicos, y el transporte terrestre y aeroespacial. La continua reducción del tamaño de los transistores ha permitido reducir su consumo y aumentar su frecuencia de funcionamiento, obteniendo por ello un mayor rendimiento global. Sin embargo, estas mismas características que mejoran el rendimiento del sistema, afectan negativamente a su confiabilidad. El uso de transistores de tamaño reducido, bajo consumo y alta velocidad, está incrementando la diversidad de fallos que pueden afectar al sistema y su probabilidad de aparición. Por lo tanto, existe un gran interés en desarrollar nuevas y eficientes técnicas para evaluar la confiabilidad, en presencia de fallos, de sistemas fabricados mediante tecnologías submicrónicas. Este problema puede abordarse por medio de la introducción deliberada de fallos en el sistema, técnica conocida como inyección de fallos. En este contexto, la inyección basada en modelos resulta muy interesante, ya que permite evaluar la confiabilidad del sistema en las primeras etapas de su ciclo de desarrollo, reduciendo por tanto el coste asociado a la corrección de errores. Sin embargo, el tiempo de simulación de modelos grandes y complejos imposibilita su aplicación en un gran número de ocasiones. Esta tesis se centra en el uso de dispositivos lógicos programables de tipo FPGA (Field-Programmable Gate Arrays) para acelerar los experimentos de inyección de fallos basados en simulación por medio de su implementación en hardware reconfigurable. Para ello, se extiende la investigación existente en inyección de fallos basada en FPGA en dos direcciones distintas: i) se realiza un estudio de las tecnologías submicrónicas existentes para obtener un conjunto representativo de modelos de fallos transitoriosAndrés Martínez, DD. (2007). Speeding-up model-based fault injection of deep-submicron CMOS fault models through dynamic and partially reconfigurable FPGAS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1943Palanci

An empirical evaluation of High-Level Synthesis languages and tools for database acceleration

High Level Synthesis (HLS) languages and tools are emerging as the most promising technique to make FPGAs more accessible to software developers. Nevertheless, picking the most suitable HLS for a certain class of algorithms depends on requirements such as area and throughput, as well as on programmer experience. In this paper, we explore the different trade-offs present when using a representative set of HLS tools in the context of Database Management Systems (DBMS) acceleration. More specifically, we conduct an empirical analysis of four representative frameworks (Bluespec SystemVerilog, Altera OpenCL, LegUp and Chisel) that we utilize to accelerate commonly-used database algorithms such as sorting, the median operator, and hash joins. Through our implementation experience and empirical results for database acceleration, we conclude that the selection of the most suitable HLS depends on a set of orthogonal characteristics, which we highlight for each HLS framework.Peer ReviewedPostprint (author’s final draft

Probabilistic performance modelling when using partial reconfiguration to accelerate streaming applications with non-deterministic task scheduling

Many streaming applications composed of multiple tasks self-adapt their tasks’ execution at runtime as response to the processed data. This type of application promises a better solution to context switches at the cost of a non-deterministic task scheduling. Partial reconfiguration is a unique feature of FPGAs that not only offers a higher resource reuse but also performance improvements when properly applied. In this paper, a probabilistic approach is used to estimate the acceleration of streaming applications with unknown task schedule thanks to the application of partial reconfiguration. This novel approach provides insights in the feasible acceleration when partially reconfiguring regions of the FPGA are partially reconfigured in order to exploit the available resources by processing multiple tasks in parallel. Moreover, the impact of how different strategies or heuristics affect to the final performance is included in this analysis. As a result, not only an estimation of the achievable acceleration is obtained, but also a guide at the design stage when searching for the highest performance

Acceleration of ListNet for ranking using reconfigurable architecture

Document ranking is used to order query results by relevance with ranking models. ListNet is a well-known ranking approach for constructing and training learning-to-rank models. Compared with traditional learning approaches, ListNet delivers better accuracy, but is computationally too expensive to learn models with large data sets due to the large number of permutations and documents involved in computing the gradients. Currently, the long training time limits the practicality of ListNet in ranking applications such as breaking news search and stock prediction, and this situation is getting worse with the increase in data-set size. In order to tackle the challenge of long training time, this thesis optimises the ListNet algorithm, and designs hardware accelerators for learning the ListNet algorithm using Field Programmable Gate Arrays (FPGAs), making the algorithm more practical for real-world application. The contributions of this thesis include: 1) A novel computation method of the ListNet algorithm for ranking. The proposed computation method exposes more fine-grained parallelism for FPGA implementation. 2) A weighted sampling method that takes into account the ranking positions, along with an effective quantisation method based on FPGA devices. The proposed design achieves a 4.42x improvement over GPU implementation speed, while still guaranteeing the accuracy. 3) A full reconfigurable architecture for the ListNet training using multiple bitstream kernels. The proposed method achieves a higher model accuracy than pure fixed point training, and a better throughput than pure floating point training. This thesis has resulted in the acceleration of the ListNet algorithm for ranking using FPGAs by applying the above techniques. Significant improvements in speed have been achieved in this work against CPU and GPU implementations.Open Acces

Using Partial Reconfiguration for SoC Design and Implementation

Most reconfigurable systems rely on FPGA technology. Among these ones, those which permit dynamic and partial reconfiguration, offer added benefits in flexibility, in-field device upgrade, improved design and manufacturing time, and even, in some cases, power consumption reductions. However, dynamic reconfiguration is a complex task, and the real benefits of its use in real applications have been often questioned. This paper presents an overview of the partial reconfiguration technique application, along with four original applications. The main goal of these applications is to test several architectures with different flexibility and, to search for the partial reconfiguration "killing application", that is, the application that better demonstrates the benefits of today reconfigurable systems based on commercial FPGAs. Therefore, the presented applications are rather a proof of concept, than fully operative and closed systems. First, a brief introduction to the partial reconfigurable systems application topic has been included. After that, the descriptions of the created reconfigurable systems are presented: first, an on-chip communications emulation framework, second, an on chip debugging system, third, a wireless sensor network reconfigurable node and finally, a remote reconfigurable client-server device. Each application is described in a separate section of the paper along with some test and results. General conclusions are included at the end of the pape

Models for Co-Design of Heterogeneous Dynamically Reconfigurable SoCs

International audienceThe design of Systems-on-Chip is becoming an increasing difficult challenge due to the continuous exponential evolution of the targeted complex architectures and applications. Thus, seamless methodologies and tools are required to resolve the SoC design issues. This chapter presents a high level component based approach for expressing system reconfigurability in SoC co-design. A generic model of reactive control is presented for Gaspard2, a SoC co-design framework. Control integration in different levels of the framework is explored along with a comparison of their advantages and disadvantages. Afterwards, control integration at another high abstraction level is investigated which proves to be more beneficial then the other alternatives. This integration allows to integrate reconfigurability features in modern SoCs. Finally a case study is presented for validation purposes. The presented works are based on Model-Driven Engineering (MDE) and UML MARTE profile for modeling and analysis of real-time embedded systems

MARTE based design flow for Partially Reconfigurable Systems-on-Chips

International audienceSystems-on-Chip (SoCs) are considered an integral solution for designing embedded systems, for targeting complex intensive parallel computation applications. As advances in SoC technology permit integration of increasing number of hardware resources on a single chip, the targeted application domains such as software-defined radio are become increasingly sophisticated. The fallout of this complexity is that the system design, particularly software design, does not evolve at the same pace as that of hardware leading to a significant productivity gap. Adaptivity and reconfigurability are also critical issues for SoCs which must be able to cope with end user environment and requirements