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
INFN Roma "Sapienza", Italy
arXiv:1505.03015 [cs.DC], (12 May 2015)
@article{paolucci2015power,
title={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},
author={Paolucci, Pier Stanislao and Ammendola, Roberto and Biagioni, Andrea and Frezza, Ottorino and Cicero, Francesca Lo and Lonardo, Alessandro and Martinelli, Michele and Pastorelli, Elena and Simula, Francesco and Vicini, Piero},
year={2015},
month={may},
archivePrefix={"arXiv"},
primaryClass={cs.DC}
}
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.
May 15, 2015 by hgpu
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