Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms.
Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms.
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DOI：
10.3389/fnins.2015.00491
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发表时间：
2015
影响因子：
4.3
通讯作者：
Schmuker M
中科院分区：
文献类型：
--
作者：
Diamond A;Nowotny T;Schmuker M
神经形态计算使用神经元电路模型来解决计算问题。神经形态硬件系统现在正变得越来越普遍，“神经形态算法”正在开发中。随着它们在一般研究环境中的部署逐渐成熟，在它们要解决的应用程序的环境中评估和比较它们变得很重要。这不仅应包括任务性能，还应包括实施的简易性、处理速度、可伸缩性和能效。在这里，我们报告了我们在三个不同的平台上实现生物启发的多变量分类尖峰网络的实践经验：混合数字/模拟Spikey系统，基于数字尖峰的Spinnaker系统，以及针对并行GPU硬件的元编译器Genn。我们使用标准的手写数字分类任务来评估性能。我们发现，虽然每个平台需要不同的实现方法，但分类性能保持一致。这表明，所有三种实现都能够测试模型解决任务的能力，而不是暴露固有的平台限制，尽管在接近容量时出现了差异。关于执行速度和功耗，我们发现，对于每个平台，很大一部分计算时间都花在神经形态设备之外的主机上。时间被花费在准备模型、编码适当的输入尖峰数据、移位数据和解码尖峰编码结果的一系列组合上。这也是消耗了很大一部分总功率的地方，最明显的是Spinnaker和Spikey系统。我们的结论是，被评估的专用硬件系统的仿真效率优势很容易在主机与设备通信过多或计算的非神经元部分失去。这些结果强调了优化主机-设备通信架构以实现可扩展性、最大吞吐量和最小延迟的必要性。此外，我们的结果表明，在设计和实现用于高效神经形态计算的网络时，应特别注意最小化主机与设备之间的通信。
Neuromorphic computing employs models of neuronal circuits to solve computing problems. Neuromorphic hardware systems are now becoming more widely available and “neuromorphic algorithms” are being developed. As they are maturing toward deployment in general research environments, it becomes important to assess and compare them in the context of the applications they are meant to solve. This should encompass not just task performance, but also ease of implementation, speed of processing, scalability, and power efficiency. Here, we report our practical experience of implementing a bio-inspired, spiking network for multivariate classification on three different platforms: the hybrid digital/analog Spikey system, the digital spike-based SpiNNaker system, and GeNN, a meta-compiler for parallel GPU hardware. We assess performance using a standard hand-written digit classification task. We found that whilst a different implementation approach was required for each platform, classification performances remained in line. This suggests that all three implementations were able to exercise the model's ability to solve the task rather than exposing inherent platform limits, although differences emerged when capacity was approached. With respect to execution speed and power consumption, we found that for each platform a large fraction of the computing time was spent outside of the neuromorphic device, on the host machine. Time was spent in a range of combinations of preparing the model, encoding suitable input spiking data, shifting data, and decoding spike-encoded results. This is also where a large proportion of the total power was consumed, most markedly for the SpiNNaker and Spikey systems. We conclude that the simulation efficiency advantage of the assessed specialized hardware systems is easily lost in excessive host-device communication, or non-neuronal parts of the computation. These results emphasize the need to optimize the host-device communication architecture for scalability, maximum throughput, and minimum latency. Moreover, our results indicate that special attention should be paid to minimize host-device communication when designing and implementing networks for efficient neuromorphic computing.