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Powerline Communication for Enhanced Connectivity in Neuromorphic Systems

机译:电力线通信可增强神经形态系统的连通性

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Neuromorphic computing (NC) has acquired tremendous interest because of its ability to overcome the limitations of von-Neumann systems in data-intensive applications. NC systems are inspired from the human brain, which combine storage (synapse) and compute (neuron) to circumvent the memory bottlenecks in von-Neumann computing. The human brain consists of densely connected neurons, where each neuron can connect to thousands of synapses. Such dense connectivity enables hierarchical learning that enables high classification accuracies in NC systems, such as spiking neural networks (SNNs). Past research has focused on many-core architectures that implement synapses with memristive crossbars to overcome the memory bottlenecks and enable efficient compute. However, mimicking brainlike connectivity poses significant challenges. This is because the typical computation cores in a many-core architecture are connected with network-on-chip (NOC), which have high power consumption. In this paper, we propose a power line communication (PLC)-based architecture built with memristive crossbars for SNNs. PLC can use the on-chip power lines augmented with low-overhead transceiver to communicate data between neurons efficiently. Hence, PLC can enable dense connectivity required in SNNs while preserving the efficiency of memristive crossbars. We perform evaluations on SNNs ranging in scale from 1 to 10 M synapses to demonstrate the efficiency of PLC-based NC system. We also propose a hybrid PLC-NOC-based design which can achieve high throughput along with high energy efficiency.
机译:由于神经形态计算(NC)能够克服von-Neumann系统在数据密集型应用程序中的局限性,因此引起了极大的兴趣。 NC系统的灵感来自人脑,它结合了存储(突触)和计算(神经元)来规避von-Neumann计算中的存储瓶颈。人脑由紧密连接的神经元组成,每个神经元可以连接到数千个突触。这种密集的连通性实现了分层学习,该分层学习可在尖峰神经网络(SNN)等NC系统中实现较高的分类精度。过去的研究集中在许多核心架构上,这些架构使用忆阻交叉开关实现突触,从而克服内存瓶颈并实现高效计算。然而,模仿大脑般的连通性提出了巨大的挑战。这是因为多核体系结构中的典型计算核与具有高功耗的片上网络(NOC)连接。在本文中,我们提出了一种基于电力线通信(PLC)的体系结构,该体系结构具有用于SNN的忆阻交叉开关。 PLC可以使用扩展了低开销收发器的片上电源线来有效地在神经元之间传递数据。因此,PLC可以实现SNN中所需的密集连接,同时保持忆阻交叉开关的效率。我们对范围从1到10 M突触的SNN进行评估,以证明基于PLC的NC系统的效率。我们还提出了一种基于PLC-NOC的混合设计,该设计可实现高吞吐量和高能效。

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