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，但类似人类的智能信息处理仍不可能实现。在这个项目中，我们的目标是在首席研究员提出的心理VLSI大脑模型的基础上，利用最先进的硅技术构建一个类似于人类的灵活的图像识别系统。以医用X光影像诊断为试验车，成功开发了一种智能图像识别算法，展示了在某高校医院工作10年以上经验的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门用于关联图像处理的VLSI芯片来展示。为在未来的研究项目中建立一个实时响应的低功耗人脑计算超大规模集成电路系统奠定了坚实的基础。在感知和理解某事时，回忆过去的…更多与当前事件最相关的经验发生在我们的大脑中，作为最基本的处理过程。在这种心理大脑模型的启发下，我们开发了最大似然搜索VLSI引擎，我们称之为联想处理器。为了满足不同的需求，各种类型的高性能联合处理器芯片已经被开发出来，它们同时使用了数字和模拟CMOS技术。其中，我们首次开发了铁电联想存储器作为移动应用的候选材料。该项目最重要的成果是发明了一种新的图像表示算法，称为投影主边分布(PPED)，使我们能够使用联想处理器进行稳健的图像识别。该算法通过一个专用的VLSI芯片来提取图像中的大部分原始特征(我们称之为Piclet)来形成特征向量。PPED表示很好地保留了人类对图像在向量空间中相似性的感知，同时实现了图像数据的实质性降维，从而为使用关联处理器的硬件识别系统提供了最有利的特征。较少

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