Journal Article

FZJ-2022-01365

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Simulating the Cortical Microcircuit Significantly Faster Than Real Time on the IBM INC-3000 Neural Supercomputer

Heittmann, A. (Corresponding author)FZJ* ; Psychou, G.FZJ* ; Trensch, G.FZJ* ; Cox, C. E. ; Wilcke, W. W. ; Diesmann, M.FZJ* ; Noll, T. G.FZJ*

2022
Frontiers Research Foundation Lausanne

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Please use a persistent id in citations: http://hdl.handle.net/2128/30741  doi:10.3389/fnins.2021.728460

Abstract: This article employs the new IBM INC-3000 prototype FPGA-based neural supercomputer to implement a widely used model of the cortical microcircuit. With approximately 80,000 neurons and 300 Million synapses this model has become a benchmark network for comparing simulation architectures with regard to performance. To the best of our knowledge, the achieved speed-up factor is 2.4 times larger than the highest speed-up factor reported in the literature and four times larger than biological real time demonstrating the potential of FPGA systems for neural modeling. The work was performed at Jülich Research Centre in Germany and the INC-3000 was built at the IBM Almaden Research Center in San Jose, CA, United States. For the simulation of the microcircuit only the programmable logic part of the FPGA nodes are used. All arithmetic is implemented with single-floating point precision. The original microcircuit network with linear LIF neurons and current-based exponential-decay-, alpha-function- as well as beta-function-shaped synapses was simulated using exact exponential integration as ODE solver method. In order to demonstrate the flexibility of the approach, additionally networks with non-linear neuron models (AdEx, Izhikevich) and conductance-based synapses were simulated, applying Runge–Kutta and Parker–Sochacki solver methods. In all cases, the simulation-time speed-up factor did not decrease by more than a very few percent. It finally turns out that the speed-up factor is essentially limited by the latency of the INC-3000 communication system.

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Contributing Institute(s):

1. JARA Institut Green IT (PGI-10)
2. Computational and Systems Neuroscience (INM-6)
3. Jara-Institut Brain structure-function relationships (INM-10)
4. Theoretical Neuroscience (IAS-6)
5. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)
2. Advanced Computing Architectures (aca_20190115) (aca_20190115)
3. 5122 - Future Computing & Big Data Systems (POF4-512) (POF4-512)
4. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2022

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