An Intelligent Image-Recognition VLSI System Employing Neuron-MOS Feature Extracting Circuitry

尽管数字计算机技术取得了惊人的进步，但像人类一样的智能信息处理还不可能。在这个项目中，我们的目标是建立一个类似人类的灵活的图像识别系统，使用国家的最先进的硅技术的基础上提出的“心理超大规模集成电路大脑模型”的首席研究员。以医学X光图像诊断为试验载体，成功开发了智能图像识别算法，展示了经某大学医院10年以上经验专家认可的诊断结果。此外，这种系统的实时响应能力已经通过开发专用于关联图像处理的超大规模集成电路芯片而被展示。因此，我们为未来的研究项目建立了一个实时响应的低功耗类人脑计算VLSI系统奠定了坚实的基础。 关于我们 与当前事件最相关的经验作为最基本的处理过程发生在我们的大脑中。受这种心理大脑模型的启发，我们开发了最大似然搜索VLSI引擎，我们称之为联想处理器。各种类型的高性能关联处理器芯片已开发使用数字和模拟CMOS技术，以满足不同的需求。其中，我们首次开发了一种铁电联想存储器，作为移动的应用的候选者。在该项目中最重要的成就是发明了一种新的图像表示算法，称为PPED（投影主边分布），这使我们能够使用联想处理器进行鲁棒的图像识别。该算法基于一个专用的VLSI芯片提取图像中最原始的特征（我们称之为“piclet”），形成特征向量。PPED表示很好地保留了人类感知的相似性之间的图像在向量空间中，同时实现了大量的降维的图像数据，从而提供了最有利的功能，硬件识别系统使用关联处理器。少

Despite the phenomenal advancement in the digital computer technology, human-like intelligent information processing is not yet possible. In this project, we have aimed at building a human-like flexible image recognition system using the state-of-the-art silicon technology based upon the "Psychological VLSI Brain Model" proposed by the head investigator. Taking the medical X-ray picture diagnosis as a test vehicle, we have succeeded in developing an intelligent image recognition algorithm, demonstrating the diagnostic results approved by experts having more than 10 years of experience in a university hospital. Furthermore, the real-time response capability of such systems has been shown by developing VLSI chips dedicated to associative image processing. As a result, we have established a solid foundation on which we can build a real-time-response low-power human-like-brain-computing VLSI system in the future research project.In perceiving and understanding something, recalling the past … More experience most relevant to the current event is occurring in our brains as the most basic processing. Being inspired by such a psychological brain model, we have developed the maximum-likelihood-search VLSI engine, which we call associative processors. Various types of high-performance associative processor chips have been developed using both digital and analog CMOS technologies to meet varying needs. Among them, we have developed a ferroelectric associative memory for the first time as a candidate for use in mobile applications.The most important achievement in the project is the invention of a new image representation algorithm called PPED (projected Principal-Edge Distribution), which has enabled us to carry out robust image recognition using the associative processors. The algorithm bases on the extraction of most primitive features in the image (which we called "piclet") by a dedicated VLSI chip to form feature vectors. The PPED representation very well preserves the human perception of similarity among images in the vector space while achieving the substantial dimensionality reduction in the image data, thus providing the most favorable feature for hardware recognition systems using associative processors. Less