论文标题

通过基于混合尖峰的学习电路实施高效平衡网络

Implementing efficient balanced networks with mixed-signal spike-based learning circuits

论文作者

Büchel, Julian, Kakon, Jonathan, Perez, Michel, Indiveri, Giacomo

论文摘要

有效的平衡网络(EBN)是尖峰神经元网络,其中兴奋性和抑制性突触电流在短时间内平衡,从而导致理想的编码属性,例如高编码精度,低触发率和分布式信息表示。出于这些好处,希望在低功率神经形态处理器中实施此类网络。但是,模拟混合信号神经形态电路中设备不匹配的程度使使用预训练的EBN具有挑战性(即使不是不可能)的使用。为了克服这个问题,我们制定了一项适合片上实施的新型本地学习规则,该规则将随机连接的尖峰神经元网络驱动到紧密平衡的政权中。在这里,我们介绍实施此规则的集成电路,并在低级电路模拟中证明其预期行为。我们提出的方法为在模拟混合信号神经形态硬件上紧密平衡网络的系统级实现铺平了道路。由于其编码属性和稀疏活动,神经形态电子EBN将非常适合极端边缘计算应用,这些应用需要低延迟,超低功耗,并且不能依靠云计算来处理数据处理。

Efficient Balanced Networks (EBNs) are networks of spiking neurons in which excitatory and inhibitory synaptic currents are balanced on a short timescale, leading to desirable coding properties such as high encoding precision, low firing rates, and distributed information representation. It is for these benefits that it would be desirable to implement such networks in low-power neuromorphic processors. However, the degree of device mismatch in analog mixed-signal neuromorphic circuits renders the use of pre-trained EBNs challenging, if not impossible. To overcome this issue, we developed a novel local learning rule suitable for on-chip implementation that drives a randomly connected network of spiking neurons into a tightly balanced regime. Here we present the integrated circuits that implement this rule and demonstrate their expected behaviour in low-level circuit simulations. Our proposed method paves the way towards a system-level implementation of tightly balanced networks on analog mixed-signal neuromorphic hardware. Thanks to their coding properties and sparse activity, neuromorphic electronic EBNs will be ideally suited for extreme-edge computing applications that require low-latency, ultra-low power consumption and which cannot rely on cloud computing for data processing.

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