Vanarse Anup, Osseiran Adam, Rassau Alexander
School of Engineering, Edith Cowan University Joondalup, WA, Australia.
Front Neurosci. 2016 Mar 29;10:115. doi: 10.3389/fnins.2016.00115. eCollection 2016.
Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI (analog Very Large Scale Integration) and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data. The foundation for research in neuromorphic sensors was laid more than two decades ago, but recent developments in understanding of biological sensing and advanced electronics, have stimulated research on sophisticated neuromorphic sensors that provide numerous advantages over conventional sensors. In this paper, we review the current state-of-the-art in neuromorphic implementation of vision, auditory, and olfactory sensors and identify key contributions across these fields. Bringing together these key contributions we suggest a future research direction for further development of the neuromorphic sensing field.
传统的视觉、听觉和嗅觉传感器会产生大量冗余数据,因此往往会消耗过多的能量。为了解决这些缺点,人们开发了神经形态传感器。这些传感器使用超大规模集成电路(模拟超大规模集成电路)模仿感觉器官的神经生物学结构,并产生异步脉冲输出,以类似于神经信号的方式表示传感信息。由于能够从稀疏的捕获数据中提取有用的传感信息,这使得功耗大大降低。神经形态传感器的研究基础早在二十多年前就已奠定,但对生物传感和先进电子学的最新理解进展,激发了对复杂神经形态传感器的研究,这种传感器比传统传感器具有许多优势。在本文中,我们回顾了神经形态视觉、听觉和嗅觉传感器的当前技术现状,并确定了这些领域的关键贡献。综合这些关键贡献,我们提出了神经形态传感领域进一步发展的未来研究方向。