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This short note regards a comparison of instantaneous power, total energyconsumption, execution time and energetic cost per synaptic event of a spikingneural network simulator (DPSNN-STDP) distributed on MPI processes whenexecuted either on an embedded platform (based on a dual socket quad-core ARMplatform) or a server platform (INTEL-based quad-core dual socket platform). Wealso compare the measure with those reported by leading custom and semi-customdesigns: TrueNorth and SpiNNaker. In summary, we observed that: 1- we spent 2.2micro-Joule per simulated event on the "embedded platform", approx. 4.4 timeslower than what was spent by the "server platform"; 2- the instantaneous powerconsumption of the "embedded platform" was 14.4 times better than the "server"one; 3- the server platform is a factor 3.3 faster. The "embedded platform" ismade of NVIDIA Jetson TK1 boards, interconnected by Ethernet, each mounting aTegra K1 chip including a quad-core ARM Cortex-A15 at 2.3GHz. The "serverplatform" is based on dual-socket quad-core Intel Xeon CPUs (E5620 at 2.4GHz).The measures were obtained with the DPSNN-STDP simulator (Distributed Simulatorof Polychronous Spiking Neural Network with synaptic Spike Timing DependentPlasticity) developed by INFN, that already proved its efficient scalabilityand execution speed-up on hundreds of similar "server" cores and MPI processes,applied to neural nets composed of several billions of synapses.

Power, Energy and Speed of Embedded and Server Multi-Cores applied to  Distributed Simulation of Spiking Neural Networks: ARM in NVIDIA Tegra vs  Intel Xeon quad-cores