Self-Adaptation for Availability in CPU-FPGA Systems Under Soft Errors

We introduce a model-based reliability estimation to preserve application availability in CPU-FPGA systems exposed to soft errors under varying environment conditions. The estimation is used as an in-system method to select a suitable configuration for changing radiation conditions. This allows systems to autonomously adapt their configuration in order to balance between reliability and performance. Such a self-adaptation goes beyond the state-of-the-art, where adaptation relies on preplanned reactive mode changes. By autonomously evaluating new configurations, our self-adaptation process is capable of increasing the availability by selecting the configuration with the desired application reliabilities for the current environment conditions

Integrated design optimization methods for optimal sensor placement and cooling system architecture design for electro-thermal systems

Dynamic thermal management plays a very important role in the design and development of electro-thermal systems as these become more active and complex in terms of their functionalities. In highly power dense electronic systems, the heat is concentrated over small spatial domains. Thermal energy dissipation in any electrified system increases the temperature and might cause component failure, degradation of heat sensitive materials, thermal burnouts and failure of active devices. So thermal management needs to be done both accurately (by thermal monitoring using sensors) and efficiently (by applying fluid-based cooling techniques). In this work, two important aspects of dynamic thermal management of a highly dense power electronic system have been investigated. The first aspect is the problem of optimal temperature sensor placement for accurate thermal monitoring aimed toward achieving thermally-aware electrified systems. Strategic placement of temperature sensors can improve the accuracy of real-time temperature distribution estimates. Enhanced temperature estimation supports increased power throughput and density because Power Electronic Systems (PESs) can be operated in a less conservative manner while still preventing thermal failure. This work presents new methods for temperature sensor placement for 2- and 3-dimensional PESs that 1) improve computational efficiency (by orders of magnitude in at least one case), 2) support use of more accurate evaluation metrics, and 3) are scalable to high-dimension sensor placement problems. These new methods are tested via sensor placement studies based on a 2-kW, 60Hz, single-phase, Flying Capacitor Multi-Level (FCML) prototype inverter. Information-based metrics are derived from a reduced-order Resistance-Capacitance (RC) lumped parameter thermal model. Other more general metrics and system models are possible through application of a new continuous relaxation strategy introduced here for placement representation. A new linear Programming (LP) formulation is presented that is compatible with a particular type of information-based metric. This LP strategy is demonstrated to support the efficient solution of finely-discretized large-scale placement problems. The optimal sensor locations obtained from these methods were tested via physical experiments. The new methods and results presented here may aid the development of thermally-aware PESs with significantly enhanced capabilities. The second aspect is to design optimal fluid-based thermal management architectures through enumerative methods that help operate the system efficiently within its operating temperature limits using the minimum feasible coolant flow level. Expert intuition based on physics knowledge and vast experience may not be adequate to identify optimal thermal management designs as systems increase in size and complexity. This work also presents a design framework supporting comprehensive exploration of a class of single-phase fluid-based cooling architectures. The candidate cooling system architectures are represented using labeled rooted tree graphs. Dynamic models are automatically generated from these trees using a graph-based thermal modeling framework. Optimal performance is determined by solving an appropriate fluid flow distribution problem, handling temperature constraints in the presence of exogenous heat loads. Rigorous case studies are performed in simulation, with components having variable sets of heat loads and temperature constraints. Results include optimization of thermal endurance for an enumerated set of 4,051 architectures. In addition, cooling system architectures capable of steady-state operation under a given loading are identified. Optimization of the cooling system design has been done subject to a representative mission, consisting of multiple time-varying loads. Work presented in this thesis clearly shows that the transient effects of heat loads are expected to have important impacts on design decisions when compared to steady-state operating conditions