Future Memcomputing Arrays for Next Generation Computer Vision

高分辨率图像的实时处理对于许多新的人工智能来说是必不可少的。技术.然而，目前的计算需求，成本和能源需求对于许多主流应用程序来说都是令人望而却步的，更不用说处理系统缺乏可移植性或延迟问题，如果计算是通过云来完成的。必须采取新的办法来满足这些具有挑战性的要求。高度并行计算被广泛认为是实现实时成像所需性能水平的唯一可行方法。然而，使用传统半导体CMOS方法实现这一点所需的电路元件的复杂性和数量影响了整个系统的速度和光学分辨率。忆阻器是一种两端电子器件，由于其制造简单、成本低廉、工作功耗低、可实现超高密度、非易失性数据存储等优点，引起了人们的广泛研究兴趣。近年来，忆阻器性能有了相当大的进步。已经实现了非常高水平的耐久性（1200亿次循环）和保持力（>10年），并且已经实现了可扩展性低至2 nm的超高密度交叉阵列。然而，正是它们模仿生物突触的记忆和学习特性的能力，以及它们生产新一代超高性能人工智能设备的潜力，激发了研究人员对这些非凡设备的兴趣。许多基本的神经元功能已被证明，忆阻器阵列已被证明可以有效地在模拟域中进行处理，消除了与大量矢量矩阵运算相关的计算瓶颈。随着世界寻求新技术来规避摩尔定律的终结和传统冯·诺依曼计算的问题（其在处理和传输信息的方式中具有固有的瓶颈），这与器件可靠性的最新改进相结合，为它们的未来使用提供了有希望的前景。或者通过施加光来改变它们的开关特性。这些被称为光学忆阻器的设备的发展是由于几个潜在的好处而出现的。光学系统不受电子噪声和电容耦合效应的影响，这些因素限制了传统电子设备的运行速度。忆阻器技术与光学系统的结合提供了高速数据路由的额外优势，同时消耗很少的功率，提出了一种基于光学忆阻器（OM）和细胞非线性网络（CNN）的新型计算机视觉识别系统。它利用OM的独特能力来检测光和存储信息，同时还利用CNN同时处理所有细胞中信息的能力。这将实现超快的实时（内存和并行）计算。该方法与标准的视觉识别系统形成了鲜明的对比，后者本质上受到数据传输瓶颈和缓慢的串行信息处理的限制。因此，这项研究将为新一代超快、高分辨率视觉识别系统铺平道路，这些系统将影响当前广泛的社会需求（例如更安全的自动驾驶、更好的安全系统）和医学领域的众多应用（例如用于早期癌症诊断的高通量细胞成像）。

Real-time processing of high-resolution images is essential for many new A.I. technologies. However, currently the computational needs, cost and energy requirements are prohibitive for many mainstream applications, not to mention the lack of portability of the processing systems or latency issues if computing is done via the cloud. New approaches must be adopted to meet these challenging demands. Highly parallel computing is widely agreed as the only viable way to achieve the level of performance needed for real-time imaging. However, the complexity and number of circuit components required to achieve this with traditional semiconductor CMOS approaches impacts the overall system's speed and optical resolution. Thus, there is a need to develop new types of circuit components that are specifically designed for neuromorphic computing.Memristors are two terminal electronic devices that have attracted intense research interest owing to their simple fabrication, low-cost manufacture, low power operation and their capacity for ultra-high density, non-volatile data storage. In recent years, memristor performances have advanced considerably. Very high levels of endurance (120 billion cycles) and retention (>10 years) have been achieved, and ultra-high-density cross-bar arrays have been realized with scalability down to 2 nm. However, it is their ability to emulate the memory and learning properties of biological synapses and their potential to produce a new generation of ultra-high performance artificial intelligent devices that has ignited researchers' interest in these remarkable devices. Many basic neuronal functions have been demonstrated and memristor arrays have been shown to efficiently carry out processing in the analogue domain, removing the computational bottlenecks associated with the large number of vector-matrix operations. This combined with recent improvements in device reliability gives a promising outlook for their future use as the world seeks new technologies to circumvent the end of Moore's law and the problems of traditional von Neumann computing, which has inherent bottlenecks in the way information is processed and transported.Recently there has been a drive towards the development of memristor devices that can be read, written or have their switching characteristics modified by the application of light. The development of these devices, termed Optical Memristors, arises due to several potential benefits. Optical systems are free of sources of electronic noise and capacitive coupling effects, which limit the operating speed of traditional electronic devices. The combination of memristor technology with optical systems offers the additional advantage of high-speed data routing while consuming little power, as well as integration as a building block within future optical computer architectures.In this proposal a new type of computer vision recognition system is proposed based on optical memristors (OM) and cellular nonlinear networks (CNN) that leverages the unique capacity of OM's to detect light and store information while also exploiting CNN's ability to simultaneously process the information in all cells at once. This will enable ultra-fast real-time (in-memory and parallel) computation. The approach outlined contrasts with standard vision recognition systems which are inherently limited by data transfer bottlenecks and the slow, serial processing of information. This research will therefore pave the way to a new generation of ultra-fast, high-resolution vision recognition systems that will impact a wide range of current societal needs (e.g. safer autonomous driving, better security systems) and numerous applications in medicine (e.g. high throughput cell imaging for early cancer diagnostics).