Christian Plessl

Virtualizing Hardware with Multi-context Reconfigurable Arrays

In contrast to processors, current reconfigurable devices totally lack programming models that would allow for device independent compilation and forward compatibility. The key to overcome this limitations is hardware virtualization. In this paper, we resort to a macro-pipelined execution model to achieve hardware virtualization for data streaming applications. As a hardware implementation we present a hybrid multi-context architecture that attaches a coarse-grained reconfigurable array to a host CPU. A co-simulation framework enables cycle-accurate simulation of the complete architecture. As a case study we map an FIR filter to our virtualized hardware model and evaluate different designs. We discuss the impact of the number of contexts and the feature of context state on the speedup and the CPU load.

ReconOS: An Operating System Approach for Reconfigurable Computing

The ReconOS operating system for reconfigurable computing offers a unified multithreaded programming model and OS services for threads executing in software and threads mapped to reconfigurable hardware. The OS interface lets hardware threads interact with software threads using well-known mechanisms such as semaphores, mutexes, condition variables, and message queues. By semantically integrating hardware accelerators into a standard OS environment, ReconOS allows for rapid design-space exploration, supports a structured application development process, and improves the portability of applications between different reconfigurable computing systems.

Partially Reconfigurable Cores for Xilinx Virtex

Recent generations of high-density and high-speed FPGAs provide a sufficient capacity for implementing complete configurable systems on a chip (CSoCs). Hybrid CPUs that combine standard CPU cores with reconfigurable coprocessors are an important subclass of CSoCs. With partially reconfigurable FPGAs, coprocessors can be loaded on demand while the CPU remains running. However, the lack of high-level design tools for partial reconfiguration makes practical implementations a challenging task.In this paper, we introduce a design flow to implement hybrid processors on Xilinx Virtex. The design flow is based on two techniques, virtual sockets and feed-through components, and can efficiently generate partial configurations from industry-quality cores. We discuss the design flow and present a fully operational audio streaming prototype to demonstrate its feasibility.

Measuring and Predicting Temperature Distributions on FPGAs at Run-Time

In the next decades, hybrid multi-cores will be the predominant architecture for reconfigurable FPGA-based systems. Temperature-aware thread mapping strategies are key for providing dependability in such systems. These strategies rely on measuring the temperature distribution and redicting the thermal behavior of the system when there are changes to the hardware and software running on the FPGA. While there are a number of tools that use thermal models to predict temperature distributions at design time, these tools lack the flexibility to autonomously adjust to changing FPGA configurations. To address this problem we propose a temperature-aware system that empowers FPGA-based reconfigurable multi-cores to autonomously predict the on-chip temperature distribution for pro-active thread remapping. Our system obtains temperature measurements through a self-calibrating grid of sensors and uses area constrained heat-generating circuits in order to generate spatial and temporal temperature gradients. The generated temperature variations are then used to learn the free parameters of the systems thermal model. The system thus acquires an understanding of its own thermal characteristics. We implemented an FPGA system containing a net of 144 temperature sensors on a Xilinx Virtex-6 LX240T FPGA that is aware of its thermal model. Finally, we show that the temperature predictions vary less than 0.72 degree C on average compared to the measured temperature distributions at run-time.

The case for reconfigurable hardware in wearable computing

Wearable computers are embedded into the mobile environment of their users. A design challenge for wearable systems is to combine the high performance required for tasks such as video decoding with the low energy consumption required to maximise battery runtimes and the flexibility demanded by the dynamics of the environment and the applications. In this paper, we demonstrate that reconfigurable hardware technology is able to answer this challenge. We present the concept and the prototype implementation of an autonomous wearable unit with reconfigurable modules (WURM). We discuss experiments that show the uses of reconfigurable hardware in WURM: ASICs-on-demand and adaptive interfaces. Finally, we present an experiment with an operating system layer for WURM.

Reconfigurable hardware in wearable computing nodes

Wearable computers are embedded into the mobile environment of the human body. A design challenge for wearable systems is to combine the high performance required for tasks such as video decoding with low energy consumption required to maximize battery runtimes and the flexibility demanded by the dynamics of the environment and the applications. In this paper, we demonstrate that reconfigurable hardware technology is able to answer this challenge. We present the concept and the prototype implementation of an autonomous wearable unit with reconfigurable modules (WURM). We discuss two experiments that show the uses of reconfigurable hardware in WURM: ASICs-on-demand and adaptive interfaces. Finally, we develop and evaluate task placement techniques used in the operating system layer of WURM.

Zippy - a coarse-grained reconfigurable array with support for hardware virtualization

This paper motivates the use of hardware visualization on coarse-grained reconfigurable architectures. We introduce Zippy, a coarse-grained multi-context hybrid CPU with architectural support for efficient hardware virtualization. The architectural details and the corresponding tool flow are outlined. As a case study, we compare the non-virtualized and the virtualized execution of an ADPCM decoder.

System-level performance evaluation of reconfigurable processors

Abstract Reconfigurable architectures that tightly integrate a standard CPU core with a field-programmable hardware structure have recently been receiving increased attention. The design of such a hybrid reconfigurable processor involves a multitude of design decisions regarding the field-programmable structure as well as its system integration with the CPU core. Determining the impact of these design decisions on the overall system performance is a challenging task. In this paper, we first present a framework for the cycle-accurate performance evaluation of hybrid reconfigurable processors on the system level. Then, we discuss a reconfigurable processor for data-streaming applications, which attaches a coarse-grained reconfigurable unit to the coprocessor interface of a standard embedded CPU core. By means of a case study we evaluate the system-level impact of certain design features for the reconfigurable unit, such as multiple contexts, register replication, and hardware context scheduling. The results illustrate that a system-level evaluation framework is of paramount importance for studying the architectural trade-offs and optimizing design parameters for reconfigurable processors.

EvoCaches: Application-specific Adaptation of Cache Mappings

In this work we present EvoCache, a novel approach for implementing application-specific caches. The key innovation of EvoCache is to make the function that maps memory addresses from the CPU address space to cache indices programmable. We support arbitrary Boolean mapping functions that are implemented within a small reconfigurable logic fabric. For finding suitable cache mapping functions we rely on techniques from the evolvable hardware domain and utilize an evolutionary optimization procedure. We evaluate the use of EvoCache in an embedded processor for two specific applications (JPEG and BZIP2 compression) with respect to execution time, cache miss rate and energy consumption. We show that the evolvable hardware approach for optimizing the cache functions not only significantly improves the cache performance for the training data used during optimization, but that the evolved mapping functions generalize very well. Compared to a conventional cache architecture, EvoCache applied to test data achieves a reduction in execution time of up to 14.31% for JPEG (10.98% for BZIP2), and in energy consumption by 16.43% for JPEG (10.70% for BZIP2). We also discuss the integration of EvoCache into the operating system and show that the area and delay overheads introduced by EvoCache are acceptable.

FELIX: a high-throughput network approach for interfacing to front end electronics for ATLAS upgrades

The ATLAS experiment at CERN is planning full deployment of a new unified optical link technology for connecting detector front end electronics on the timescale of the LHC Run 4 (2025). It is estimated that roughly 8000 GBT (GigaBit Transceiver) links, with transfer rates up to 10.24 Gbps, will replace existing links used for readout, detector control and distribution of timing and trigger information. A new class of devices will be needed to interface many GBT links to the rest of the trigger, data-acquisition and detector control systems. In this paper FELIX (Front End LInk eXchange) is presented, a PC-based device to route data from and to multiple GBT links via a high-performance general purpose network capable of a total throughput up to O(20 Tbps). FELIX implies architectural changes to the ATLAS data acquisition system, such as the use of industry standard COTS components early in the DAQ chain. Additionally the design and implementation of a FELIX demonstration platform is presented and hardware and software aspects will be discussed.