Survivability Modeling and Resource Planning for Self-Repairing Reconfigurable Device Fabrics

Abstract

A resilient system design problem is formulated as the quantification of uncommitted reconfigurable resources required for a system of components to survive its lifetime within mission availability specifications. We show that this survivability metric can be calculated according to the residual functionality obtained from pools of dynamically configurable elements constituting the amorphous resource pool (ARP). The ARP is depleted based on the failure rate to replenish the functionality lost in a reconfigurable fabric due to the occurrence of permanent faults during the mission lifetime. While genetic algorithms are selected for the reparation method, any probabilistic or deterministic active repair strategy is covered without loss of generality. Parameters of this model are correlated with reliability specifications of Xilinx Virtex-4 field programmable gate array devices, which are then utilized for MCNC benchmark circuits along with a realistic space mission. Calculation of the spare fabric resources which must be budgeted for a mission, maximum mission lifetime, and repair policy parameters are realized using the proposed probabilistic survivability model for soft computing-based repair strategies.

State-Burst Feedback Control for Fault Recovery of Input/State Asynchronous Sequential Machines

“…Whereas the present study focuses on transient faults of which influence on the machine vanishes right after its occurrence, there exist other kinds of faults differing in the durability of their influences. If the adverse effect persists for a finite time after initial occurrence, the fault is termed an intermittent fault [33]; if the effect remains indefinitely (or irreversible), it is termed a permanent fault [34]. For input/state ASMs, fault recovery is impossible for either intermittent or permanent faults since immediate return to the original state cannot be implemented.…”

Radiation-hardened MRAM-based LUT for non-volatile FPGA soft error mitigation with multi-node upset tolerance

“…The dynamic behavior of free layer in SHE-MTJ devices can be described using a modified Landau-Lifshitz-Gilbert (LLG) equation, as expressed in (6)(7)(8)(9) [37,38]:…”

“…Moreover, and are polar and azimuthal angles, respectively. Substituting (7-9) into (6) results in a nonlinear system of two differential equations expressed in (10) [37,38]:…”

“…These include the cumulative impact of aggressive voltage scaling which reduces the voltage headroom available to mask errors, and the continued miniaturization of deeply-scaled complementary metal oxide semiconductor (CMOS)-based computing technology [5]. Applications which are especially susceptible due to factors of the mission criticality, the number and density of sensitive devices, and the environmental exposures they endure are autonomous systems utilizing high capacity fine-grained configurable components, such as field programming gate arrays (FPGAs) [6].…”

SNRA: A Spintronic Neuromorphic Reconfigurable Array for In-Circuit Training and Evaluation of Deep Belief Networks

“…Namley, a tractable in-field reconfiguration-based approach is developed to leverage in-field configurability to mitigate the impact of process variation. Reconfigurable fabrics are characterized by their fabric flexibility, which allows realization of logic elements at medium and fine granularities, as well as in-field adaptability, which can be leveraged to realize variation tolerance and fault resiliency as widely-demonstrated for CMOSbased approaches such as [5], [6]. Utilizing reconfigurable computing by applying hardware and time redundancy to the digital circuits offers promising and robust techniques for addressing the above-mentioned reliability challenges.…”