Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this article, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on hierarchical temporal memory that inherently offers online learning, resiliency, and fault tolerance. Architecturally, it is a full custom mixed-signal design with an underlying digital communication scheme and analog computational modules. Therefore, the proposed system features reconfigurability, real-time processing, low power consumption, and low-latency processing. The proposed architecture is benchmarked to predict on real-world streaming data. The network's mean absolute percentage error on the mixed-signal system is 1.129 X lower compared to its baseline algorithm model. This reduction can be attributed to device non-idealities and probabilistic formation of synaptic connections. We demonstrate that the combined effect of Hebbian learning and network sparsity also plays a major role in extending the overall network lifespan. We also illustrate that the system offers 3.46 X reduction in latency and 77.02 X reduction in power consumption when compared to a custom CMOS digital design implemented at the same technology node. By employing specific low power techniques, such as clock gating, we observe 161.37 X reduction in power consumption.

中文翻译：

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用于模式识别和序列预测的端到端忆阻 HTM 系统

从流输入中学习和预测的神经形态系统在普遍的边缘计算及其应用中具有重要的前景。在本文中，提出了一种处理边缘时空信息的神经形态系统。从算法上讲，该系统基于分层时间记忆，它本质上提供在线学习、弹性和容错能力。在架构上，它是一个完全定制的混合信号设计，具有底层数字通信方案和模拟计算模块。因此，所提出的系统具有可重构性、实时处理、低功耗和低延迟处理的特点。所提出的架构进行了基准测试，以预测真实世界的流数据。网络在混合信号系统上的平均绝对百分比误差为 1。与其基线算法模型相比低 129 倍。这种减少可归因于设备的非理想性和突触连接的概率形成。我们证明了 Hebbian 学习和网络稀疏性的组合效应在延长整体网络寿命方面也起着重要作用。我们还说明，与在同一技术节点上实施的定制 CMOS 数字设计相比，该系统的延迟降低了 3.46 倍，功耗降低了 77.02 倍。通过采用特定的低功耗技术，例如时钟门控，我们观察到功耗降低了 161.37 倍。我们证明了 Hebbian 学习和网络稀疏性的组合效应在延长整体网络寿命方面也起着重要作用。我们还说明，与在同一技术节点上实施的定制 CMOS 数字设计相比，该系统的延迟降低了 3.46 倍，功耗降低了 77.02 倍。通过采用特定的低功耗技术，例如时钟门控，我们观察到功耗降低了 161.37 倍。我们证明了 Hebbian 学习和网络稀疏性的组合效应在延长整体网络寿命方面也起着重要作用。我们还说明，与在同一技术节点上实施的定制 CMOS 数字设计相比，该系统的延迟降低了 3.46 倍，功耗降低了 77.02 倍。通过采用特定的低功耗技术，例如时钟门控，我们观察到功耗降低了 161.37 倍。