A Hardware Task-Graph Scheduler for Reconfigurable Multi-tasking Systems

Reconfigurable hardware can be used to build a multitasking system where tasks are assigned to HW resources at run-time according to the requirements of the running applications. These tasks are frequently represented as direct acyclic graphs and their execution is typically controlled by an embedded processor that schedules the graph execution. In order to improve the efficiency of the system, the scheduler can apply prefetch and reuse techniques that can greatly reduce the reconfiguration latencies. For an embedded processor all these computations represent a heavy computational load that can significantly reduce the system performance. To overcome this problem we have implemented a HW scheduler using reconfigurable resources. In addition we have implemented both prefetch and replacement techniques that obtain as good results as previous complex SW approaches, while demanding just a few clock cycles to carry out the computations. We consider that the HW cost of the system (in our experiments 3% of a Virtex-II PRO xc2vp30 FPGA) is affordable taking into account the great efficiency of the techniques applied to hide the reconfiguration latency and the negligible run-time penalty introduced by the scheduler computations

A Replacement Technique to Maximize Task Reuse in Reconfigurable Systems

Dynamically reconfigurable hardware is a promising technology that combines in the same device both the high performance and the flexibility that many recent applications demand. However, one of its main drawbacks is the reconfiguration overhead, which involves important delays in the task execution, usually in the order of hundreds of milliseconds, as well as high energy consumption. One of the most powerful ways to tackle this problem is configuration reuse, since reusing a task does not involve any reconfiguration overhead. In this paper we propose a configuration replacement policy for reconfigurable systems that maximizes task reuse in highly dynamic environments. We have integrated this policy in an external taskgraph execution manager that applies task prefetch by loading and executing the tasks as soon as possible (ASAP). However, we have also modified this ASAP technique in order to make the replacements more flexible, by taking into account the mobility of the tasks and delaying some of the reconfigurations. In addition, this replacement policy is a hybrid design-time/run-time approach, which performs the bulk of the computations at design time in order to save run-time computations. Our results illustrate that the proposed strategy outperforms other state-ofthe-art replacement policies in terms of reuse rates and achieves near-optimal reconfiguration overhead reductions. In addition, by performing the bulk of the computations at design time, we reduce the execution time of the replacement technique by 10 times with respect to an equivalent purely run-time one

A Task-Graph Execution Manager for Reconfigurable Multi-tasking Systems

Reconfigurable hardware can be used to build multi tasking systems that dynamically adapt themselves to the requirements of the running applications. This is especially useful in embedded systems, since the available resources are very limited and the reconfigurable hardware can be reused for different applications. In these systems computations are frequently represented as task graphs that are executed taking into account their internal dependencies and the task schedule. The management of the task graph execution is critical for the system performance. In this regard, we have developed two dif erent versions, a software module and a hardware architecture, of a generic task-graph execution manager for reconfigurable multi-tasking systems. The second version reduces the run-time management overheads by almost two orders of magnitude. Hence it is especially suitable for systems with exigent timing constraints. Both versions include specific support to optimize the reconfiguration process

A Hardware Implementation of a Run-Time Scheduler for Reconfigurable Systems

New generation embedded systems demand high performance, efficiency and flexibility. Reconfigurable hardware can provide all these features. However the costly reconfiguration process and the lack of management support have prevented a broader use of these resources. To solve these issues we have developed a scheduler that deals with task-graphs at run-time, steering its execution in the reconfigurable resources while carrying out both prefetch and replacement techniques that cooperate to hide most of the reconfiguration delays. In our scheduling environment task-graphs are analyzed at design-time to extract useful information. This information is used at run-time to obtain near-optimal schedules, escaping from local-optimum decisions, while only carrying out simple computations. Moreover, we have developed a hardware implementation of the scheduler that applies all the optimization techniques while introducing a delay of only a few clock cycles. In the experiments our scheduler clearly outperforms conventional run-time schedulers based on As-Soon-As-Possible techniques. In addition, our replacement policy, specially designed for reconfigurable systems, achieves almost optimal results both regarding reuse and performance

Analysis of the reconfiguration latency and energy overheads for a Xilinx Virtex-5 FPGA

In this paper we have evaluated the overhead and the tradeoffs of a set of components usually included in a system with run-time partial reconfiguration implemented on a Xilinx Virtex-5. Our analysis shows the benefits of including a scratchpad memory inside the reconfiguration controller in order to improve the efficiency of the reconfiguration process. We have designed a simple controller for this scratchpad that includes support for prefetching and caching in order to further reduce both the energy and latency overhead

Anti-Tamper Method for Field Programmable Gate Arrays Through Dynamic Reconfiguration and Decoy Circuits

As Field Programmable Gate Arrays (FPGAs) become more widely used, security concerns have been raised regarding FPGA use for cryptographic, sensitive, or proprietary data. Storing or implementing proprietary code and designs on FPGAs could result in the compromise of sensitive information if the FPGA device was physically relinquished or remotely accessible to adversaries seeking to obtain the information. Although multiple defensive measures have been implemented (and overcome), the possibility exists to create a secure design through the implementation of polymorphic Dynamically Reconfigurable FPGA (DRFPGA) circuits. Using polymorphic DRFPGAs removes the static attributes from their design; thus, substantially increasing the difficulty of successful adversarial reverse-engineering attacks. A variety of dynamically reconfigurable methodologies exist for implementation that challenge designers in the reconfigurable technology field. A Hardware Description Language (HDL) DRFPGA model is presented for use in security applications. The Very High Speed Integrated Circuit HDL (VHSIC) language was chosen to take advantage of its capabilities, which are well suited to the current research. Additionally, algorithms that explicitly support granular autonomous reconfiguration have been developed and implemented on the DRFPGA as a means of protecting its designs. Documented testing validates the reconfiguration results and compares power usage, timing, and area estimates from a conventional and DRFPGA model

Un gestor de ejecución de grafos de tareas para sistemas multitarea dinámicamente reconfigurables

El HW reconfigurable se puede utilizar para construir un sistema multitarea en el que las tareas puedan asignarse en tiempo de ejecución a los recursos reconfigurables según las necesidades de las aplicaciones. En estos sistemas, las tareas se representan normalmente como grafos de subtareas acíclicos, donde una subtarea es la unidad de planificación. Normalmente, un procesador empotrado controla la ejecución de este tipo de sistemas trabajando con estructuras de datos complejas, como grafos o listas enlazadas, cuyo manejo a menudo genera retardos en la ejecución. Además, las comunicaciones HW/SW son a menudo un cuello de botella del sistema. Por tanto resulta muy interesante reducir tanto los cálculos que realiza el procesador como las comunicaciones. Para lograr este objetivo se ha desarrollado un gestor HW que controla la ejecución de grafos de subtareas en un conjunto de unidades reconfigurables. Este gestor recibe como entrada los grafos junto con una planificación asociada a cada subtarea y garantiza su correcta ejecución sin necesidad de ninguna otra intervención por parte del procesador. Además, incluye mecanismos para optimizar la gestión de las reconfiguraciones reduciendo las penalizaciones que generan en tiempo de ejecución. [ABSTRACT] Reconfigurable HW can be used to build a hardware multitasking system where tasks can be assigned to the reconfigurable HW at run-time according to the requirements of the running applications. Normally the execution in this kind of systems is controlled by an embedded processor. In these systems tasks are frequently represented as acyclic subtask graphs, where a subtask is the basic scheduling unit that can be assigned to a reconfigurable HW. In order to control the execution of these tasks, the processor must manage at run-time complex data structures, like graphs or linked list, which may generate significant execution-time penalties. In addition, HW/SW communications are frequently a system bottleneck. Hence, it is very interesting to find a way to reduce the run-time SW computations and the HW/SW communications. To this end I have developed a HW execution manager that controls the execution of subtask graphs over a set of reconfigurable units. This manager receives as input a subtask graph coupled to a subtask schedule, and guarantees its proper execution. In addition it includes support to reduce the execution-time overhead due to reconfigurations. With this HW support the execution of task graphs can be managed efficiently generating only very small run-time penalties