{"id":13996,"date":"2015-05-15T00:23:42","date_gmt":"2015-05-14T21:23:42","guid":{"rendered":"http:\/\/hgpu.org\/?p=13996"},"modified":"2015-05-15T00:23:42","modified_gmt":"2015-05-14T21:23:42","slug":"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","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13996","title":{"rendered":"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"},"content":{"rendered":"<p>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 &quot;embedded platform&quot;, approx. 4.4 times lower than what was spent by the &quot;server platform&quot;; 2- the instantaneous power consumption of the &quot;embedded platform&quot; was 14.4 times better than the &quot;server&quot; one; 3- the server platform is a factor 3.3 faster. The &quot;embedded platform&quot; 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 &quot;server platform&quot; 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 &quot;server&quot; cores and MPI processes, applied to neural nets composed of several billions of synapses.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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). [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3],"tags":[1238,1782,242,34,632,20,386],"class_list":["post-13996","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-arm","tag-computer-science","tag-mpi","tag-neural-networks","tag-neurons-and-cognition","tag-nvidia","tag-stdp"],"views":2211,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13996","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=13996"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13996\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13996"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13996"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13996"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}