Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even superhuman performance, their energy consumption has often proved to be prohibitive in the absence of costly supercomputers. Most state-of-the-art machine-learning solutions are based on memoryless models of neurons. This is unlike the neurons in the human brain that encode and process information using temporal information in spike events. The different computing principles underlying biological neurons and how they combine together to efficiently process information is believed to be a key factor behind their superior efficiency compared to current machine-learning systems.

中文翻译：

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用于信号处理应用的低功耗神经形态硬件：架构和系统级设计方法综述

机器学习已成为执行需要监督、无监督和强化学习的复杂认知任务的主要工具。虽然由此产生的机器在某些情况下甚至表现出超人的性能，但在没有昂贵的超级计算机的情况下，它们的能耗往往被证明是令人望而却步。大多数最先进的机器学习解决方案都基于神经元的无记忆模型。这与人脑中使用尖峰事件中的时间信息编码和处理信息的神经元不同。与当前的机器学习系统相比，生物神经元背后的不同计算原理以及它们如何结合在一起以有效处理信息，被认为是其卓越效率背后的关键因素。