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技术是必不可少的。然而，目前许多主流应用对计算需求、成本和能源的要求都是令人望而却步的，更不用说通过云进行计算的处理系统缺乏可移植性或延迟问题。必须采取新的方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的性能水平的唯一可行方法。然而，用传统的半导体CMOS方法来实现这一点所需的电路元件的复杂性和数量会影响整个系统的速度和光学分辨率。因此，有必要开发专门用于神经形态计算的新型电路元件。门控管是两种终端电子器件，由于其制造简单、制造成本低、低功耗操作以及超高密度、非易失性数据存储的能力而引起了人们的浓厚研究兴趣。近年来，忆阻器的性能有了很大的提高。已经实现了非常高的耐久性(1200亿个周期)和保持(&gt；10年)，并实现了可伸缩到2 nm的超高密度交叉棒阵列。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，点燃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演示，忆阻器阵列已被证明在模拟域中有效地执行处理，消除了与大量向量矩阵运算相关的计算瓶颈。随着世界寻求新技术来绕过摩尔定律的终结和传统冯·诺伊曼计算的问题，这一点与最近器件可靠性的改进相结合，给出了它们未来应用的前景，传统的冯·诺伊曼计算在信息处理和传输的方式上具有固有的瓶颈。最近，人们一直在推动对忆阻器器件的开发，这种器件可以被读、写，或者通过施加光来改变其开关特性。这些被称为光学记忆阻器的器件的发展是由于几个潜在的好处。光学系统没有电子噪声源和电容耦合效应，这限制了传统电子设备的运行速度。将忆阻器技术与光学系统相结合，提供了高速数据路由和低功耗的额外优势，以及作为未来光计算机体系结构的构建块的集成。在该方案中，提出了一种新型的基于光学忆阻器(OM)和细胞非线性网络(CNN)的计算机视觉识别系统，该系统利用OM的独特能力来探测光和存储信息，同时利用CNN同时处理所有单元中的信息的能力。这将实现超快的实时(内存中和并行)计算。概述的方法与标准视觉识别系统形成对比，标准视觉识别系统固有地受到数据传输瓶颈和缓慢、连续的信息处理的限制。因此，这项研究将为新一代超高速、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)和大量医学应用(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).