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Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures. Indeed, the human brain includes about 10^15 synapses and 10^11 neurons activated at a mean rate of several Hz. Full brain simulation poses Exascale challenges even if simulated at the highest abstraction level. The WaveScalES experiment in the Human Brain Project (HBP) has the goal of matching experimental measures and simulations of slow waves during deep-sleep and anesthesia and the transition to other brain states. The focus is the development of dedicated large-scale parallel/distributed simulation technologies. The ExaNeSt project designs an ARM-based, low-power HPC architecture scalable to million of cores, developing a dedicated scalable interconnect system, and SWA/AW simulations are included among the driving benchmarks. At the joint between both projects is the INFN proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine. DPSNN can be configured to stress either the networking or the computation features available on the execution platforms. The simulation stresses the networking component when the neural net - composed by a relatively low number of neurons, each one projecting thousands of synapses - is distributed over a large number of hardware cores. When growing the number of neurons per core, the computation starts to be the dominating component for short range connections. This paper reports about preliminary performance results obtained on an ARM-based HPC prototype developed in the framework of the ExaNeSt project. Furthermore, a comparison is given of instantaneous power, total energy consumption, execution time and energetic cost per synaptic event of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server platforms.
We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on distributed computing platforms. Also, the speed and energy consumption of
We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range Gaussian decays. While previous studie
The cortex exhibits self-sustained highly-irregular activity even under resting conditions, whose origin and function need to be fully understood. It is believed that this can be described as an asynchronous state stemming from the balance between ex
Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unless its size grows significantly
The aim of this work is to quantitatively evaluate the impact of computation on the energy consumption on ARM MPSoC platforms, exploiting CPUs, embedded GPUs and FPGAs. One of them possibly represents the future of High Performance Computing systems: