CARLsim 4: An Open Source Library for Large Scale, Biologically Detailed Spiking Neural Network Simulation using Heterogeneous Clusters
CARLsim 4: An Open Source Library for Large Scale, Biologically Detailed Spiking Neural Network Simulation using Heterogeneous Clusters
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CARLsim 4：使用异构簇进行大规模、生物学详细的尖峰神经网络模拟的开源库
DOI：
10.1109/ijcnn.2018.8489326
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Krichmar
中科院分区：
文献类型：
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作者：
Ting;H. Kashyap;Jinwei Xing;Stanislav Listopad;Emily L. Rounds;M. Beyeler;N. Dutt;J. Krichmar
大规模尖峰神经网络（SNN）模拟由于迭代处理大量神经状态动力学和更新所需的内存和计算而具有挑战性。为了应对这些挑战，我们开发了Carlsim 4，这是一个用C ++编写的用户友好的SNN库，可以模拟大型生物学上详细的神经网络。提高了早期发行版的效率和可扩展性，本版本允许在异质计算集群中同时使用多个GPU和多个CPU核心进行仿真。基准测试结果表明，使用4 gpus对860万个神经元和44.8亿个突触进行了模拟，并在单线读取的CPU实施中进行多GPU实现的速度最高为60倍，使Carlsim 4 Wellsim在实时实时的SNN模型中，使Carlsim 4 Wellsim 4约束。此外，本版本还增加了新功能，例如泄漏的综合和射击（LIF），9参数Izhikevich，多室神经元模型以及第四阶runge runge kutta集成。
Large-scale spiking neural network (SNN) simulations are challenging to implement, due to the memory and computation required to iteratively process the large set of neural state dynamics and updates. To meet these challenges, we have developed CARLsim 4, a user-friendly SNN library written in C++ that can simulate large biologically detailed neural networks. Improving on the efficiency and scalability of earlier releases, the present release allows for the simulation using multiple GPUs and multiple CPU cores concurrently in a heterogeneous computing cluster. Benchmarking results demonstrate simulation of 8.6 million neurons and 0.48 billion synapses using 4 GPUs and up to 60x speedup for multi-GPU implementations over a single-threaded CPU implementation, making CARLsim 4 wellsuited for large-scale SNN models in the presence of real-time constraints. Additionally, the present release adds new features, such as leaky-integrate-and-fire (LIF), 9-parameter Izhikevich, multi-compartment neuron models, and fourth order Runge Kutta integration.