In this paper, we review recent work published over the last 3 years under the umbrella of Neuromorphic engineering to analyze what are the common features among such systems. We see that there is no clear consensus but each system has one or more of the following features:(1) Analog computing (2) Non vonNeumann Architecture and low-precision digital processing (3) Spiking Neural Networks (SNN) with components closely related to biology. We compare recent machine learning accelerator chips to show that indeed analog processing and reduced bit precision architectures have best throughput, energy and area efficiencies. However, pure digital architectures can also achieve quite high efficiencies by just adopting a non von-Neumann architecture. Given the design automation tools for digital hardware design, it raises a question on the likelihood of adoption of analog processing in the near future for industrial designs. Next, we argue about the importance of defining standards and choosing proper benchmarks for the progress of neuromorphic system designs and propose some desired characteristics of such benchmarks. Finally, we show brain-machine interfaces as a potential task that fulfils all the criteria of such benchmarks.

中文翻译：

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我的神经网络是神经形态的吗？神经形态工程的分类、近期趋势和未来方向

在本文中，我们回顾了最近 3 年在神经形态工程的保护下发表的工作，以分析这些系统之间的共同特征。我们看到没有明确的共识，但每个系统都有以下一个或多个特征：(1) 模拟计算 (2) 非冯诺依曼架构和低精度数字处理 (3) 尖峰神经网络 (SNN) 与组件密切相关到生物学。我们比较了最近的机器学习加速器芯片，以表明模拟处理和降低位精度的架构确实具有最佳的吞吐量、能量和面积效率。然而，纯数字架构也可以通过采用非冯诺依曼架构来实现相当高的效率。鉴于数字硬件设计的设计自动化工具，它提出了一个问题，即在不久的将来工业设计采用模拟处理的可能性。接下来，我们讨论为神经形态系统设计的进展定义标准和选择合适的基准的重要性，并提出这些基准的一些理想特征。最后，我们将脑机接口展示为满足此类基准测试所有标准的潜在任务。