Novel non-Von-Neumann solutions have raised based on artificial intelligence (AI) such as the neuromorphic spiking processors in either analog or digital domain. This paper proposes to study the deep neural network feasibility using ultra-low-power eNeuron. The trade-offs in terms of deep learning capabilities and energy efficiency are highlighted. A linear fit model is found in the region of high energy efficiency of neuromorphic components. Thus, deep learning and energy efficiency mutually exclusive if those neuromorphic components are used.

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This paper presents a simple analog neuromorphic system in microelectronics suitable for an audio source localization problem. The receptor can find the relative position of an audio source by the information of angles and distances according to acquire acoustic signals. In this paper, we focus on the angle detection, but some information of the distance is also presented. This paper also presents the development of some alternatives to the most important circuit blocks in neuromorphic systems: the neurons and the synapses. The results are validated in two different levels: the system level and the transistor level.