I-Corps: Neuromorphic device derived from resistive switching system

这个I-Corps项目的更广泛的影响/商业潜力是，由于可靠的神经形态芯片（其中神经网络被蚀刻到硅中），促进了广泛的行业在先进的基于计算的应用方面的创新。这项技术可能会加速机器学习的进步，并最终通过克服冯诺依曼瓶颈实现计算架构的突破-其中固有的延迟源于处理器和存储器的分离。通过该项目技术的神经形态特征，机器学习可以在设计和能耗方面以前所未有的速度和效率进行。因此，基于密集机器学习的技术，如人脸识别、自动驾驶和其他人工智能领域，将大幅提升。该项目的成功执行还将促进利用人工智能和/或神经形态计算的新兴应用的广泛商业化。这个I-Corps项目利用了最近与高度可控的神经形态设备相关的创新研究。该技术基于基于氧化物的电阻式随机存取存储器（ReRAM），这是一种基于电刺激引起的电阻变化的非易失性存储器，主要是通过每个单元中所谓的纳米级导电丝的形成和破裂。ReRAM细胞与神经形态系统的内在相似性引发了最近相关研究的热潮。然而，仍然存在相当大的技术问题，包括在操作电压、电池电流和操作速度方面的宽的电池到电池变化。对物理切换机制的不清楚理解排除了用于商业设备的可靠单元的合理设计。建议的新方案，其中的不可控性显着降低，预计将实现商业化neuromorphic devices.This奖项的突破，体现了美国国家科学基金会的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The broader impact/commercial potential of this I-Corps project is in facilitating innovations in a wide range of industries working on advanced computing-based applications as the result of a reliable neuromorphic chip (where neural networks are etched into silicon). This technology may accelerate the advance of machine learning and ultimately realize a breakthrough in computing architecture by overcoming the Von Neumann bottleneck -- where the intrinsic latency originated from the separation of processor and memory. Empowered by neuromorphic features of this project's technology, machine learning may be carried out with unprecedented speed and efficiency in design and energy consumption. Hence, intensive machine learning based technologies such as face recognition, autonomous driving and other areas of artificial intelligence will be advanced by a significant margin. A successful execution of the project will also facilitate widespread commercialization of emerging applications utilizing artificial intelligence and/or neuromorphic computing. This I-Corps project leverages the recent innovative research related to highly controllable neuromorphic devices. The technology is based on the oxide-based resistive random access memory (ReRAM), a non-volatile memory based upon electrical stimuli-induced resistance changes, mostly by the formation and rupture of so-called nanoscale conducting filaments in each cell. The intrinsic resemblance of ReRAM cells to neuromorphic systems triggered a recent boom in related research. However, there are still considerable technical issues including wide cell-to-cell variations in operation voltage, cell current and operation speed. Unclear understanding of physical switching mechanisms precludes a rationale design of reliable cells for a commercial device. The proposed novel scheme, in which the uncontrollability is significantly minimized, is expected to achieve a breakthrough in realizing commercializable neuromorphic devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.