  This short note regards a comparison of instantaneous power, total energy
consumption, execution time and energetic cost per synaptic event of a spiking
neural network simulator (DPSNN-STDP) distributed on MPI processes when
executed either on an embedded platform (based on a dual socket quad-core ARM
platform) or a server platform (INTEL-based quad-core dual socket platform). We
also compare the measure with those reported by leading custom and semi-custom
designs: TrueNorth and SpiNNaker. In summary, we observed that: 1- we spent 2.2
micro-Joule per simulated event on the "embedded platform", approx. 4.4 times
lower than what was spent by the "server platform"; 2- the instantaneous power
consumption 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" is
made of NVIDIA Jetson TK1 boards, interconnected by Ethernet, each mounting a
Tegra K1 chip including a quad-core ARM Cortex-A15 at 2.3GHz. The "server
platform" is based on dual-socket quad-core Intel Xeon CPUs (E5620 at 2.4GHz).
The measures were obtained with the DPSNN-STDP simulator (Distributed Simulator
of Polychronous Spiking Neural Network with synaptic Spike Timing Dependent
Plasticity) developed by INFN, that already proved its efficient scalability
and execution speed-up on hundreds of similar "server" cores and MPI processes,
applied to neural nets composed of several billions of synapses.
