PFI:AIR - TT: Memory Processing Unit: A Low Power Processor for Analytics Applications

PFI:AIR - TT：内存处理单元：用于分析应用的低功耗处理器

• 批准号: 1701099
• 负责人: Karthikeyan Sankaralingam
• 金额: $ 20万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1701099&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Memory; Processing

This PFI: AIR Technology Translation project focuses on translating 3D chip-stacking technology to develop a new microprocessor architecture called Memory Processing Unit (MPU). The new architecture offers the promise of faster, more energy efficient calculations than current solutions. This is important because there is a large body of applications such as deep-learning, big-data analytics, and data science that all require significant processing capability. While capability requirements continue to increase to meet the needs of these new applications, the rate of improvement of power efficiency of the microprocessors is decreasing (in other words, the trends predicted by Moore's Law are beginning to slow). This project will result in a prototype chip-design of the MPU with complete software API (Application Programming Interface) for end-to-end application demonstrations based on real-time speech recognition and big-data analytics for Internet search capabilities. The MPU includes the following unique features: an energy-efficient simple core implementation (including exploration of mechanisms for pipeline organization, virtual-memory, and coherence), an implementation that connects 128 such cores together, cores connected through 3D links to memory, and end-to-end software implementation. Compared to state-of-art server chips in the market, the MPU architecture provides two-fold to 12-fold calculation speedup while reducing energy consumption by 10-fold. This project addresses the following two technology gaps as it translates from research discovery toward commercial application: a) the difficulty of developing end-to end software on this new highly concurrent architecture and the determination of the mechanisms required, and b) Extensive exploration of various application domains (initially demonstrating speech recognition and internet search capabilities) to determine quantitatively the benefits of this architecture over the state-of-the-art. Personnel involved in this project, including graduate students and undergraduates, will receive innovation and entrepreneurship experiences through the technology commercialization activities, customer interviews, and business development. In addition the team will work with entrepreneurship programs like D2P at UW-Madison.

该PFI：空气技术翻译项目致力于翻译3D芯片堆积技术，以开发一种新的微处理器体系结构，称为内存处理单元（MPU）。与当前解决方案相比，新的体系结构提供了更快，更节能的计算的希望。这很重要，因为有大量的应用，例如深度学习，大数据分析和数据科学，都需要重要的处理能力。尽管能力要求继续增加以满足这些新应用的需求，但微处理器功率效率的提高速度正在减少（换句话说，摩尔定律预测的趋势开始放缓）。该项目将通过实时语音识别和Internet搜索功能的Big-DATA Analytics提供完整的软件API（应用程序编程接口），以实现MPU的原型芯片设计。 MPU包含以下独特功能：节能简单的核心实现（包括探索管道组织的机制，虚拟记忆和连贯性），该实现将128个这样的内核连接在一起，通过3D链接通过内存连接到内存，以及端到端的软件实现。与市场上最先进的服务器芯片相比，MPU架构提供了两到12倍的计算加速，同时将能耗降低了10倍。该项目解决了以下两个技术差距，因为它从研究发现转化为商业应用：a）在这种新的高度并发体系结构和确定所需机制的确定方面的端到端软件的困难，b）广泛探索各种应用程序域（最初证明语音识别和互联网搜索能力），以证明语音识别和互联网搜索能力来确定该架构的构建范围，以确定该建筑的好处。参与该项目的人员，包括研究生和本科生，将通过技术商业化活动，客户访谈和业务发展获得创新和创业经验。此外，团队将使用UW-Madison的D2P等企业家计划。