A Communication Infrastructure for Emulating Large-Scale Neural Networks Models

机译：用于模拟大规模神经网络模型的通信基础设施

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This paper presents the SEPELYNS architecture that permits to interconnect multiple spiking neurons focused on hardware implementations. SEPELYNS can connect millions of neurons with thousands of synapses per neuron in a layered fabric that provides some capabilities such as connectivity, expansion, flexibility, bio-plausibility and reusing of resources that allows simulation of very large networks. We present the three layers of this architecture (neuronal, network adapters and networks on chip layers) and explain its performance parameters such as throughput, latency and hardware resources. Some application examples of large neural networks on SEPELYNS are studied; these will show that use of on-chip parallel networks could permit the hardware simulation of populations of spiking neurons.

机译：本文介绍了允许互连专注于硬件实现的多个尖峰神经元的综合体系结构。 Sepelyns可以将数百万神经元连接到分层结构中的数千个突触，提供一些能力，例如连接，扩展，灵活性，生物合理性和资源的重用，允许模拟非常大的网络。我们介绍了这个架构的三层（芯片层上的神经元，网络适配器和网络），并解释了其性能参数，例如吞吐量，延迟和硬件资源。研究了Sepelyns上大型神经网络的一些应用实例;这些将显示，使用片上并行网络可以允许尖刺神经元的群体的硬件模拟。

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来源
《International Conference on Artificial Neural Networks》|2012年||共8页
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作者
Andres Gaona Barrera; Manuel Moreno Arostegui;
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中图分类 TP3-53;
关键词
Spiking Neuronal Networks; Networks on Chip; Neurodynamics;

机译：尖峰神经元网络;芯片网络;神经动力学;

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A Communication Infrastructure for Emulating Large-Scale Neural Networks Models

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