Onur Derin

Online task remapping strategies for fault-tolerant Network-on-Chip multiprocessors

As CMOS technology scales down into the deep-submicron domain, the aspects of fault tolerance in complex Networks-on-Chip (NoCs) architectures are assuming an increasing relevance. Task remapping is a software based solution for dealing with permanent failures in processing elements in the NoC. In this work, we formulate the optimal task mapping problem for mesh-based NoC multiprocessors with deterministic routing as an integer linear programming (ILP) problem with the objective of minimizing the communication traffic in the system and the total execution time of the application. We find the optimal mappings at design time for all scenarios where single-faults occur in the processing nodes. We propose heuristics for the online task remapping problem and compare their performance with the optimal solutions.

Adaptivity support for MPSoCs based on process migration in polyhedral process networks

System adaptivity is becoming an important feature of modern embedded multiprocessor systems. To achieve the goal of system adaptivity when executing Polyhedral Process Networks (PPNs) on a generic tiled Network-on-Chip (NoC) MPSoC platform, we propose an approach to enable the run-time migration of processes among the available platform resources. In our approach, process migration is allowed by a middleware layer which comprises two main components. The first component concerns the inter-tile data communication between processes. We develop and evaluate a number of different communication approaches which implement the semantics of the PPN model of computation on a generic NoC platform. The presented communication approaches do not depend on the mapping of processes and have been implemented on a Network-on-Chip multiprocessor platform prototyped on an FPGA. Their comparison in terms of the introduced overhead is presented in two case studies with different communication characteristics. The second middleware component allows the actual run-time migration of PPN processes. To this end, we propose and evaluate a process migrationmechanism which leverages the PPN model of computation to guarantee a predictable and efficient migration procedure. The efficiency and applicability of the proposed migration mechanism is shown in a real-life case study.

System Adaptivity and Fault-Tolerance in NoC-based MPSoCs: The MADNESS Project Approach

Modern embedded systems increasingly require adaptive run-time management. The system may adapt the mapping of the applications in order to accommodate the current workload conditions, to balance load for efficient resource utilization, to meet quality of service agreements, to avoid thermal hot-spots and to reduce power consumption. As the possibility of experiencing run-time faults becomes increasingly relevant with deep-sub-micron technology nodes, in the scope of the MADNESS project, we focus particularly on the problem of graceful degradation by dynamic remapping in presence of run-time faults. In this paper, we summarize the major results achieved in the MADNESS project until now regarding the system adaptivity and fault tolerant processing. We report the first results of the integration between platform level and middleware level support for adaptivity and fault tolerance. A case study demonstrates the survival ability of the system via a low-overhead process migration mechanism and a near-optimal online remapping heuristic.

A middleware approach to achieving fault tolerance of Kahn process networks on networks on chips

Kahn process networks (KPNs) is a distributed model of computation used for describing systems where streams of data are transformed by processes executing in sequence or parallel. Autonomous processes communicate through unbounded FIFO channels in absence of a global scheduler. In this work, we propose a task-aware middleware concept that allows adaptivity in KPN implemented over a Network on Chip (NoC). We also list our ideas on the development of a simulation platform as an initial step towards creating fault tolerance strategies for KPNs applications running on NoCs. In doing that, we extend our SACRE (Self-Adaptive Component Run Time Environment) framework by integrating it with an open source NoC simulator, Noxim. We evaluate the overhead that the middleware brings to the the total execution time and to the total amount of data transferred in the NoC. With this work, we also provide a methodology that can help in identifying the requirements and implementing fault tolerance and adaptivity support on real platforms.

A system-level approach to adaptivity and fault-tolerance in NoC-based MPSoCs: The MADNESS project

Modern embedded systems increasingly require adaptive run-time management of available resources. One method for supporting adaptivity is to implement run-time application mapping. The system may adapt the mapping of the applications in order to accommodate the current workload conditions, to balance the computing load for efficient resource utilization, to meet quality of service agreements, to avoid thermal hot-spots, and to reduce power consumption. As the possibility of experiencing run-time faults becomes increasingly relevant with deep-sub-micron technology nodes, in the scope of the MADNESS project, we focused particularly on the problem of graceful degradation by dynamic remapping in presence of run-time faults. In this paper, we summarize the major results achieved in the MADNESS project regarding the system adaptivity and fault-tolerant processing. We report the results of the integration between platform level and middleware level support for adaptivity and fault-tolerance. Two case studies demonstrate the survival ability of the system via a low-overhead process migration mechanism and by taking near optimal remapping decisions at run-time.

Coordinated management of hardware and software self-adaptivity

Self-adaptivity is the capability of a system to adapt itself dynamically to achieve its goals. Self-adaptive systems will be widely used in the future both to efficiently use system resources and to ease the management of complex systems. The frameworks for self-adaptivity developed so far usually concentrate either on self-adaptive software or on self-adaptive hardware, but not both. In this paper, we propose a model of self-adaptive systems and we describe how to manage self-adaptivity at all levels (both hardware and software) by means of a decentralized control algorithm. The key advantage of decentralized control is in the simplicity of the local controllers. Simulation results are provided to show the main characteristics of the model and to discuss it.

Middleware approaches for adaptivity of Kahn Process Networks on Networks-on-Chip

We investigate and propose a number of different middleware approaches, namely virtual connector, virtual connector with variable rate, and request-driven, which implement the semantics of Kahn Process Networks on Network-on-Chip architectures. All of the presented solutions allow for run-time system adaptivity. We implement the approaches on a Network-on-Chip multiprocessor platform prototyped on an FPGA. Their comparison in terms of the introduced overhead is presented on two case studies with different communication characteristics. We found out that the virtual connector mechanism outperforms other approaches in the communication-intensive application. In the other case study, which has a higher computation/communication ratio, the middleware approaches show similar performance.

Simulation of a self-adaptive run-time environment with hardware and software components

In this paper we describe a new way for simulating self-adaptive systems developed by relying on a component-based approach, this approach proves to be useful both in easing self-adaptivity and in providing the ability to mix hardware and software elements. Our simulation method is based on SACRE (Self-Adaptive Component Run-time Environment), a framework we have defined in Java for simulating self-adaptive systems.

Enabling self-adaptivity in component-based streaming applications

Self-adaptivity is the capability of a system to adapt itself dynamically to achieve its goals. By means of this mechanism the system is able to autonomously modify its behavior or the way in which applications are run and implemented to achieve the goals set. In this paper we propose a framework that uses a componentbased approach to implement self-adaptivity at application level. By using this mechanism, the framework provides the ability to perform both adaptation on the structure of the application (i.e., how the components are connected together) and on internal parameters of each component. At application level, there is a mechanism to monitor different parameters and to check whether the system is meeting the assigned goals or not. A controller drives adaptations when goals are not met.

Towards self-adaptive KPN applications on NoC-based MPSoCs

Self-adaptivity is the ability of a system to adapt itself dynamically to internal and external changes. Such a capability helps systems to meet the performance and quality goals, while judiciously using available resources. In this paper, we propose a framework to implement application level self-adaptation capabilities in KPN applications running on NoC-based MPSoCs. The monitorcontroller-adapter mechanism is used at the application level. The monitor measures various parameters to check whether the system meets the assigned goals. The controller takes decisions to steer the system towards the goal, which are applied by the adapters. The proposed framework requires minimal modifications to the application code and offers ease of integration. It incorporates a generic adaptation controller based on fuzzy logic. We present the MJPEG encoder as a case study to demonstrate the effectiveness of the approach. Our results show that even if the parameters of the fuzzy controller are not tuned optimally, the adaptation convergence is achieved within reasonable time and error limits. Moreover, the incurred steady-state overhead due to the framework is 4% for average frame-rate, 3.5% for average bit-rate, and 0.5% for additional control data introduced in the network.