论文标题
我的神经网络神经形态吗？分类学，最新趋势和神经形态工程的未来方向
Is my Neural Network Neuromorphic? Taxonomy, Recent Trends and Future Directions in Neuromorphic Engineering
论文作者
论文摘要
在本文中，我们回顾了最近三年在神经形态工程的保护下发表的最新工作，以分析此类系统中的常见特征。我们看到没有明确的共识，但是每个系统都具有以下一个或多个功能：（1）模拟计算（2）非Vonneumann架构和低精确的数字处理（3）尖峰神经网络（SNN），其组件与生物学紧密相关。我们比较了最近的机器学习加速器芯片，以表明确实的模拟处理和降低的位精度架构具有最佳的吞吐量，能量和区域效率。但是，纯数字架构也可以通过采用非von-Neumann架构来实现很高的效率。鉴于用于数字硬件设计的设计自动化工具，它提出了一个问题，即在不久的将来采用模拟处理的可能性。接下来，我们争论定义标准并为神经形态系统设计的进步选择适当的基准的重要性，并提出了此类基准测试的一些理想特征。最后，我们将脑机界面显示为符合此类基准的所有标准的潜在任务。
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.
