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In this paper, we review recent work published over the last 3 years underthe umbrella of Neuromorphic engineering to analyze what are the commonfeatures among such systems. We see that there is no clear consensus but eachsystem has one or more of the following features:(1) Analog computing (2) NonvonNeumann Architecture and low-precision digital processing (3) Spiking NeuralNetworks (SNN) with components closely related to biology. We compare recentmachine learning accelerator chips to show that indeed analog processing andreduced bit precision architectures have best throughput, energy and areaefficiencies. However, pure digital architectures can also achieve quite highefficiencies by just adopting a non von-Neumann architecture. Given the designautomation tools for digital hardware design, it raises a question on thelikelihood of adoption of analog processing in the near future for industrialdesigns. Next, we argue about the importance of defining standards and choosingproper benchmarks for the progress of neuromorphic system designs and proposesome desired characteristics of such benchmarks. Finally, we show brain-machineinterfaces as a potential task that fulfils all the criteria of suchbenchmarks.

Is my Neural Network Neuromorphic? Taxonomy, Recent Trends and Future Directions in Neuromorphic Engineering