近年来，以基于海量数据的大规模学习、数据挖掘以及新型互联网服务为代表的大数据应用成为学术界与工业界的"明星应用"。相对于传统应用，大数据处理所涉及的数据量大，强调高吞吐、强并行、低延迟等特性。然而，传统通用CPU在计算密度、访存性能、并行性开发和功耗等方面都无法满足大数据处理应用发展的要求。因此，分析传统CPU在面向大数据处理所存在的不足，研究面向大数据的高效CPU体系结构与关键技术，为研制高效大数据系统提供基础计算部件，具有重要的学术价值和应用前景。目前，面向大数据的CPU相关研究才开始起步，将三维集成新型非易失存储介质应用于CPU设计还处于概念形成阶段。本项目抓住这一契机，研究计算与存储融合的片上三维CPU体系结构，拟在重定制处理器资源、低功耗技术、编程模型硬件支持以及原型验证系统等方面取得突破，为解决阻碍通用大数据CPU发展的核心问题探索新的道路，以期在大数据系统研究中抢占先机。

In recent years, Big data applications such as large-scale data based learning, data mining, and new Internet services become the "star applications" of academia and industry. With respect to the traditional application, big data applications involve large data processing and emphazie on the characteristics of high throughput, high parallelism and low latency. However, current modern general-purpose CPU is unable to meet the high requirement of big data applications in computing density, memory access performance, parallel exploration and power consumption. Therefore, the analysis on the gap between traditional CPU and big data processing, and research on efficient CPU architecture for big data can have important academic value and application prospect, which provides basic computing component for the development of efficient big data system. Currently, CPU design for big data is still in its infancy, and the three-dimensional integrated non-volatile memory architecture is still in the conceptual stage. To seize this reasearch oppportunity, this project proposes the computation and memory fused CPU architecture using three-dimension and emerging memory technology. It tries to do a breakthrough in terms of recustomization of processor resources, low power consumption technology, hardware support for programming model, and prototype verification system. The aim is to explore new roads for solving the issues that hinder the development of CPU for big data, and to gain opportunity from its system design.