This title appears in the Scientific Report :
2018
Please use the identifier:
http://hdl.handle.net/2128/18862 in citations.
Please use the identifier: http://dx.doi.org/10.3389/fnins.2018.00291 in citations.
Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model
Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model
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 sc...
| Personal Name(s): | van Albada, Sacha (Corresponding author) |
|---|---|
| Rowley, Andrew G. / Senk, Johanna / Hopkins, Michael / Schmidt, Maximilian / Stokes, Alan B. / Lester, David R. / Diesmann, Markus / Furber, Steve B. | |
| Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Theoretical Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
| Published in: | Frontiers in neuroscience, 12 (2018) S. 291 |
| Imprint: |
Lausanne
Frontiers Research Foundation
2018
|
| PubMed ID: |
29875620 |
| DOI: |
10.3389/fnins.2018.00291 |
| Document Type: |
Journal Article |
| Research Program: |
Brain-inspired multiscale computation in neuromorphic hybrid systems The Human Brain Project Human Brain Project Specific Grant Agreement 1 Theory, modelling and simulation |
| Link: |
OpenAccess OpenAccess |
| Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.3389/fnins.2018.00291 in citations.
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| 245 | |a Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model | ||
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| 520 | |a 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 energy consumption for NEST is reached at around 144 parallel threads and 4.6 times slowdown. At this setting, NEST and SpiNNaker have a comparable energy consumption per synaptic event. Our results widen the application domain of SpiNNaker and help guide its development, showing that further optimizations such as synapse-centric network representation are necessary to enable real-time simulation of large biological neural networks. | ||
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