Communication architecture enabling 100x accelerated simulation of biological neural networks

Kauth, Kevin; Stadtmann, Tim; Brandhofer, Ruben; Sobhani, Vida; Gemmeke, Tobias

doi:10.1145/3414622.3431909

Cited by 8 publications

(14 citation statements)

References 26 publications

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“…We start this analysis by devising a baseline network topology and corresponding routing algorithm using the static simulator. In previous work, we found that long-hop connections are crucial for accelerated, large-scale neuromorphic systems that contain thousands of nodes (Kauth et al, 2020). However, for our case of 35 nodes, we have enough transceivers to realize a more closelyconnected topology that is simple to route through.…”

Section: Resultsmentioning

confidence: 98%

“…This empirical approach has been cross-validated with examples that are simple enough to be expressed in an analytical form. As one example, the bandwidth requirement in a broadcast approach for a mesh-like network topology is driven by the number of neurons per node, the firing rate, the system's acceleration factor, the number of target nodes and the size of each spike message (Kauth et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…For significantly larger clusters, a lookup in the target node, as performed in the case of broadcast, becomes difficult. Possible solutions to this problem have already been presented by Kauth et al (2020).…”

Section: Memory Bandwidthmentioning

confidence: 99%

“…The proposed framework can be utilized to constrain the design space of neuroscience simulation systems, identify the costs and bottlenecks, explore solutions and validate ideas. In our previous work, we studied suitable communication architectures—a major bottleneck in accelerated simulation of large-scale networks—utilizing the static and dynamic simulators (Kauth et al, 2020 ). Similarly, Kleijnen et al ( 2022 ) focused on simulation regarding heterogeneous neural networks and corresponding mapping algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time

Kauth

Stadtmann

Sobhani

et al. 2023

Front. Comput. Neurosci.

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IntroductionResearch in the field of computational neuroscience relies on highly capable simulation platforms. With real-time capabilities surpassed for established models like the cortical microcircuit, it is time to conceive next-generation systems: neuroscience simulators providing significant acceleration, even for larger networks with natural density, biologically plausible multi-compartment models and the modeling of long-term and structural plasticity.MethodsStressing the need for agility to adapt to new concepts or findings in the domain of neuroscience, we have developed the neuroAIx-Framework consisting of an empirical modeling tool, a virtual prototype, and a cluster of FPGA boards. This framework is designed to support and accelerate the continuous development of such platforms driven by new insights in neuroscience.ResultsBased on design space explorations using this framework, we devised and realized an FPGA cluster consisting of 35 NetFPGA SUME boards.DiscussionThis system functions as an evaluation platform for our framework. At the same time, it resulted in a fully deterministic neuroscience simulation system surpassing the state of the art in both performance and energy efficiency. It is capable of simulating the microcircuit with 20× acceleration compared to biological real-time and achieves an energy efficiency of 48nJ per synaptic event.

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Section: Resultsmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

Section: Memory Bandwidthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time

Kauth

Stadtmann

Sobhani

et al. 2023

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Kleijnen et al ( 2022 ), the performance of our simulator was validated against a numerical model (Vainbrand and Ginosar, 2011 ) and put into relation to an analytical model presented in Kauth et al ( 2020 ). However, both these models are limited to the use of homogeneous connectivity models.…”

Section: Methodsmentioning

confidence: 99%

Verification of a neuromorphic computing network simulator using experimental traffic data

et al. 2022

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Simulations are a powerful tool to explore the design space of hardware systems, offering the flexibility to analyze different designs by simply changing parameters within the simulator setup. A precondition for the effectiveness of this methodology is that the simulation results accurately represent the real system. In a previous study, we introduced a simulator specifically designed to estimate the network load and latency to be observed on the connections in neuromorphic computing (NC) systems. The simulator was shown to be especially valuable in the case of large scale heterogeneous neural networks (NNs). In this work, we compare the network load measured on a SpiNNaker board running a NN in different configurations reported in the literature to the results obtained with our simulator running the same configurations. The simulated network loads show minor differences from the values reported in the ascribed publication but fall within the margin of error, considering the generation of the test case NN based on statistics that introduced variations. Having shown that the network simulator provides representative results for this type of —biological plausible—heterogeneous NNs, it also paves the way to further use of the simulator for more complex network analyses.

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nAIxt: A Light-Weight Processor Architecture for Efficient Computation of Neuron Models

Kauth,

Lanius,

Gemmeke

2024

Lecture Notes in Computer Science

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Communication architecture enabling 100x accelerated simulation of biological neural networks

Cited by 8 publications

References 26 publications

neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time

neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time

Verification of a neuromorphic computing network simulator using experimental traffic data

nAIxt: A Light-Weight Processor Architecture for Efficient Computation of Neuron Models

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