Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System
Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System
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DOI：
10.1109/tbcas.2016.2579164
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发表时间：
2017-02-01
影响因子：
5.1
通讯作者：
Meier, Karlheinz
中科院分区：
文献类型：
--
作者：
Friedmann, Simon;Schemmel, Johannes;Meier, Karlheinz
我们介绍了一种在神经形态硬件系统中学习和可塑性的新方法的结果：为了使可实现的学习机制具有灵活性，同时保持与神经形态实现相关的高效率，我们将通用处理器与全定制模拟元件相结合。这个处理器与一个完全并行的神经形态系统并行运行，该系统由连接到模拟、连续时间神经元电路的突触阵列组成。新颖的模拟相关传感器电路并行并实时地处理每个突触的尖峰事件。处理器使用该预处理来计算新的权重，可能在其程序之后使用附加信息。因此，在一定程度上，学习规则可以在软件中定义，从而提供了很大程度的灵活性。突触作为模拟域的中枢计算基元，实现了与棘波时序相关的可塑性(STDP)相关检测。与生物时间刻度相比，我们以1000倍的速度运行，测量从几十微秒到数百微秒的时间常数。我们分析跨多个芯片的可变性，并使用乘性STDP规则演示学习。我们的结论是，作为神经科学研究和技术应用的平台，所提出的方法将使学习变得灵活和高效。
We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a general-purpose processor with full-custom analog elements. This processor is operating in parallel with a fully parallel neuromorphic system consisting of an array of synapses connected to analog, continuous time neuron circuits. Novel analog correlation sensor circuits process spike events for each synapse in parallel and in real-time. The processor uses this pre-processing to compute new weights possibly using additional information following its program. Therefore, to a certain extent, learning rules can be defined in software giving a large degree of flexibility. Synapses realize correlation detection geared towards Spike-Timing Dependent Plasticity (STDP) as central computational primitive in the analog domain. Operating at a speed-up factor of 1000 compared to biological time-scale, we measure time-constants from tens to hundreds of micro-seconds. We analyze variability across multiple chips and demonstrate learning using a multiplicative STDP rule. We conclude that the presented approach will enable flexible and efficient learning as a platform for neuroscientific research and technological applications.