ORIGINAL RESEARCH published: 23 May 2018 doi: 10.3389/fnins.2018.00291 Frontiers in Neuroscience | www.frontiersin.org 1 May 2018 | Volume 12 | Article 291 Edited by: Jorg Conradt, Technische Universität München, Germany Reviewed by: Terrence C Stewart, University of Waterloo, Canada Fabio Stefanini, Columbia University, United States *Correspondence: Sacha J. van Albada s.van.albada@fz-juelich.de Specialty section: This article was submitted to Neuromorphic Engineering, a section of the journal Frontiers in Neuroscience Received: 12 September 2017 Accepted: 13 April 2018 Published: 23 May 2018 Citation: van Albada SJ, Rowley AG, Senk J, Hopkins M, Schmidt M, Stokes AB, Lester DR, Diesmann M and Furber SB (2018) Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model. Front. Neurosci. 12:291. doi: 10.3389/fnins.2018.00291 Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model Sacha J. van Albada 1 *, Andrew G. Rowley 2 , Johanna Senk 1 , Michael Hopkins 2 , Maximilian Schmidt 1,3 , Alan B. Stokes 2 , David R. Lester 2 , Markus Diesmann 1,4,5 and Steve B. Furber 2 1 Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany, 2 Advanced Processor Technologies Group, School of Computer Science, University of Manchester, Manchester, United Kingdom, 3 Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Japan, 4 Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany, 5 Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany The digital neuromorphic hardware SpiNNaker has been developed with the aim of enabling large-scale neural network simulations in real time and with low power consumption. Real-time performance is achieved with 1 ms integration time steps, and thus applies to neural networks for which faster time scales of the dynamics can be neglected. By slowing down the simulation, shorter integration time steps and hence faster time scales, which are often biologically relevant, can be incorporated. We here describe the first full-scale simulations of a cortical microcircuit with biological time scales on SpiNNaker. Since about half the synapses onto the neurons arise within the microcircuit, larger cortical circuits have only moderately more synapses per neuron. Therefore, the full-scale microcircuit paves the way for simulating cortical circuits of arbitrary size. With approximately 80,000 neurons and 0.3 billion synapses, this model is the largest simulated on SpiNNaker to date. The scale-up is enabled by recent developments in the SpiNNaker software stack that allow simulations to be spread across multiple boards. Comparison with simulations using the NEST software on a high-performance cluster shows that both simulators can reach a similar accuracy, despite the fixed-point arithmetic of SpiNNaker, demonstrating the usability of SpiNNaker for computational neuroscience applications with biological time scales and large network size. The runtime and power consumption are also assessed for both simulators on the example of the cortical microcircuit model. To obtain an accuracy similar to that of NEST with 0.1 ms time steps, SpiNNaker requires a slowdown factor of around 20 compared to real time. The runtime for NEST saturates around 3 times real time using hybrid parallelization with MPI and multi-threading. However, achieving this runtime comes at the cost of increased power and energy consumption. The lowest total