Real-Time Olivary Neuron Simulations on Dataflow Computing Machines

Smaragdos, Georgios; Davies, Craig; Strydis, Christos; Sourdis, Ioannis; Ciobanu, Catalin Bogdan; Mencer, Oskar; Zeeuw, Chris I. De

doi:10.1007/978-3-319-07518-1_34

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…On purely dataflow neuromodeling applications, the DFE can have great benefits for both large-scale networks and real-time networks performance [24]. Even in the cases of HH neurons that include highly accurate interconnectivity modeling (disrupting the purely dataflow nature), the DFEs can accomplish greater benefits than traditional control-flow-based FPGA acceleration [20].…”

Section: Discussionmentioning

confidence: 99%

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations

et al. 2017

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The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

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Section: Discussionmentioning

confidence: 99%

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations

et al. 2017

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“…It, further, allows for applications to be implemented in a deeply pipelined fashion leading to a high computational throughput. The performance benefits due to the dataflow paradigm, when compared to the control-flow paradigm, are shown in [14]. Moreover, by programming with the MaxJ toolflow, the programming complexity is significantly reduced in comparison to using low-level (e.g.…”

Section: B Hh-model Dataflow-computing Paradigmmentioning

confidence: 99%

flexHH: A Flexible Hardware Library for Hodgkin-Huxley-Based Neural Simulations

et al. 2020

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The Hodgkin-Huxley (HH) neuron is one of the most biophysically-meaningful models used in computational neuroscience today. Ironically, the model's high experimental value is offset by its disproportional computational complexity. To such an extent that neuroscientists have either resorted to simpler models, losing precious neuron detail, or to using high-performance computing systems, to gain acceleration, for complex models. However, multicore/multinode CPU-based systems have proven too slow while FPGA-based ones have proven too time-consuming to (re)deploy to. Clearly, a solution that bridges user friendliness and high speedups is necessary. This paper presents flexHH, a flexible FPGA library implementing five popular, highly parameterizable variants of the HH neuron model. flexHH is the first crucial step towards making FPGA-based simulations of compute-intensive neural models available to neuroscientists without the debilitating penalty of re-engineering and re-synthesis. Through flexHH, the user can instantiate custom models and immediately take advantage of the acceleration without the mediation of an engineer, which has proven to be a major inhibitor to full adoption of FPGAs in neuroscience labs. In terms of performance, flexHH achieves speedups between 8×-20× compared to sequential-C implementations, while only a small drop in real-time capabilities is observed when compared to hardcoded FPGA-based versions of the models tested.

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“…The goal of our work is BrainFrame, which is a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, Intel Xeon-Phi CPUs, NVIDIA GP-GPUs, and Maxeler Dataflow Engines (DFEs). So far, we have simulated the ION model on a single-node GPU [7], Xeon-Phi [29], and DFE [30] setup as well as on a multi-node (eight-way) Xeon-Phi [1] setup. Eventually, BrainFrame will move toward multi-node heterogeneity and into the Cloud for all to access.…”

Section: Related Work: Survey Of Neural-network Simulatorsmentioning

confidence: 99%

Exploring Complex Brain-Simulation Workloads on Multi-GPU Deployments

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Smaragdos

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et al. 2019

ACM Trans. Archit. Code Optim.

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In-silico brain simulations are the de-facto tools computational neuroscientists use to understand large-scale and complex brain-function dynamics. Current brain simulators do not scale efficiently enough to largescale problem sizes (e.g., >100,000 neurons) when simulating biophysically complex neuron models. The goal of this work is to explore the use of true multi-GPU acceleration through NVIDIA's GPUDirect technology on computationally challenging brain models and to assess their scalability. The brain model used is a state-of-the-art, extended Hodgkin-Huxley, biophysically meaningful, three-compartmental model of the inferior-olivary nucleus. The Hodgkin-Huxley model is the most widely adopted conductance-based neuron representation, and thus the results from simulating this representative workload are relevant for many other brain experiments. Not only the actual network-simulation times but also the network-setup times were taken into account when designing and benchmarking the multi-GPU version, an aspect often ignored in similar previous work. Network sizes varying from 65K to 2M cells, with 10 and 1,000 synapses per neuron were executed on 8, 16, 24, and 32 GPUs. Without loss of generality, simulations were run for 100 ms of biological time. Findings indicate that communication overheads do not dominate overall execution while scaling the network size up is computationally tractable. This scalable design proves that large-network simulations of complex neural models are possible using a multi-GPU design with GPUDirect.

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Real-Time Olivary Neuron Simulations on Dataflow Computing Machines

Cited by 8 publications

References 8 publications

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations

flexHH: A Flexible Hardware Library for Hodgkin-Huxley-Based Neural Simulations

Exploring Complex Brain-Simulation Workloads on Multi-GPU Deployments

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