Mathematical models with varying degrees of complexity have been proposed and simulated in an attempt to represent the intricate mechanisms of the human neuron. One of the most biochemically realistic and analytical models, based on the Hodgkin–Huxley (HH) model, has been selected for study in this paper. In order to satisfy the model's computational demands, we present a simulator implemented on Intel Xeon Phi Knights Landing manycore processors. This high-performance platform features an x86-based architecture, allowing our implementation to be portable to other common manycore processing machines. This is reinforced by the fact that Phi adopts the popular OpenMP and MPI programming models. The simulator performance is evaluated when calculating neuronal networks of varying sizes, density and network connectivity maps. The evaluation leads to an analysis of the neuronal synaptic patterns and their impact on performance when tackling this type of workload on a multinode system. It will be shown that the simulator can calculate 100 ms of simulated brain activity for up to 2 millions of biophysically-accurate neurons and 2 billion neuronal synapses within one minute of execution time. This level of performance renders the application an efficient solution for large-scale detailed model simulation.

Reconfigurable hardware can be employed to tolerate permanent faults. Hardware components comprising a System-on-Chip can be partitioned into a handful of substitutable units interconnected with reconfigurable wires to allow isolation and replacement of faulty parts. This paper offers a probabilistic analysis of reconfigurable designs estimating for different fault densities the average number of fault-free components that can be constructed as well as the probability to guarantee a particular availability of components. Considering the area overheads of reconfigurability, we evaluate the resilience of various reconfigurable designs with different granularities. Based on this analysis, we conduct a comprehensive design-space exploration to identify the granularity mixes that maximize the fault-tolerance of a system. Our findings reveal that mixing fine-grain logic with a coarse-grain sparing approach tolerates up to 3× more permanent faults than component redundancy and 2× more than any other purely coarse-grain solution. Component redundancy is preferable at low fault densities, while coarse-grain and mixed-grain reconfigurability maximize availability at medium and high fault densities, respectively.