Stöckel Andreas, Jenzen Christoph, Thies Michael, Rückert Ulrich
Cognitronics and Sensor Systems, Cluster of Excellence Cognitive Interaction Technology, Faculty of Technology, Bielefeld UniversityBielefeld, Germany.
Front Comput Neurosci. 2017 Aug 22;11:71. doi: 10.3389/fncom.2017.00071. eCollection 2017.
Large-scale neuromorphic hardware platforms, specialized computer systems for energy efficient simulation of spiking neural networks, are being developed around the world, for example as part of the European Human Brain Project (HBP). Due to conceptual differences, a universal performance analysis of these systems in terms of runtime, accuracy and energy efficiency is non-trivial, yet indispensable for further hard- and software development. In this paper we describe a scalable benchmark based on a spiking neural network implementation of the binary neural associative memory. We treat neuromorphic hardware and software simulators as black-boxes and execute exactly the same network description across all devices. Experiments on the HBP platforms under varying configurations of the associative memory show that the presented method allows to test the quality of the neuron model implementation, and to explain significant deviations from the expected reference output.
大规模神经形态硬件平台，即用于高效能模拟脉冲神经网络的专用计算机系统，正在世界各地研发，例如作为欧洲人类大脑计划（HBP）的一部分。由于概念上的差异，要对这些系统在运行时间、准确性和能源效率方面进行通用的性能分析并非易事，但对于进一步的硬件和软件开发而言却必不可少。在本文中，我们描述了一种基于二元神经联想记忆的脉冲神经网络实现的可扩展基准测试。我们将神经形态硬件和软件模拟器视为黑箱，并在所有设备上执行完全相同的网络描述。在联想记忆的不同配置下对HBP平台进行的实验表明，所提出的方法能够测试神经元模型实现的质量，并解释与预期参考输出的显著偏差。
