ParaFPGA 2011 : high performance computing with multiple FPGAs : design, methodology and applications

ParaFPGA 2011 marks the third mini-symposium devoted to the methodology, design and implementation of parallel applications using FPGAs. The focus of the contributions is mainly on organizing parallel applications in multiple FPGAs. This includes experiences from building a supercomputer with FPGAs, automatic and dedicated balancing of different tasks on heterogeneous FPGA constellations and designing optimal interconnects between collaborating FPGAs

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems

This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system' that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS), Washington, DC, April, 200

Embedded Linux as a Platform for Dynamically Self-Reconfiguring Systems-On-Chip

We have previously argued the benefits of embedded Linux as an operating system platform for reconfigurable system-on-chip design. In this paper we describe our approach to building tools for the implementation of dynamically and self-reconfigurable systems, and show that embedded Linux is a natural and powerful platform on which to build these tools. We present examples and demonstrations that show how complex operations such as obtaining partial bit streams from remote servers and initiating reconfiguration are achieved with a single line of Linux shell script

Restoring Reliability in Fault Tolerant Reconfigurable Systems

The new generations of SRAM-based FPGAdevices, built on nanometer technology, are thepreferred choice for the implementation ofreconfigurable computing platforms. However,smaller technological scales increase theirvulnerability to manufacturing imperfections andhence to the occurrence of electromigration.Moreover, the large internal RAM (for configurationpurposes or as embedded memory blocks) makesthem more prone to soft errors.The incorporation of self-reconfigurationcapabilities in recent FPGAs, allied to the use of softand hard microprocessor cores, facilitates the offsetof these vulnerabilities by enabling the developmentof self-restoring fault tolerant reconfigurablesystems. In the methodology presented in this paper,the embedded microprocessor is also responsible forthe implementation of online self-test-and-repairstrategies, based on modular redundancy and onself-reconfiguration. The detection of faults, causedby soft or hard errors, may be followed by repairingactions, depending on the fault type. This approachleads to smoother system degradation, extending itslifetime and improving its reliability

Physical 2D Morphware and Power Reduction Methods for Everyone

Dynamic and partial reconfiguration discovers more and more the focus in academic and industrial research. Modern systems in e.g. avionic and automotive applications exploit the parallelism of hardware in order to reduce power consumption and to increase performance. State of the art reconfigurable FPGA devices allows reconfiguring parts of their architecture while the other configured architecture stays undisturbed in operation. This dynamic and partial reconfiguration allows therefore adapting the architecture to the requirements of the application while run-time. The difference to the traditional term of software and its related sequential architecture is the possibility to change the paradigm of brining the data to the respective processing elements. Dynamic and partial reconfiguration enables to bring the processing elements to the data and is therefore a new paradigm. The shift from the traditional microprocessor approaches with sequential processing of data to parallel processing reconfigurable architectures forces to introduce new paradigms with the focus on computing in time and space