Collaborative Research: CNS Core:Small:IMPERIAL: In-Memory Processing Enhanced Racetrack Inspired by Accessing Laterally

协作研究：CNS Core:Small:IMPERIAL：受横向访问启发的内存处理增强赛道

• 批准号: 2133267
• 负责人: Jingtong Hu
• 金额: $ 32万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133267&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

Next generation mobile systems require memory and storage with unprecedented density and access speed that meets strict power/energy and reliability constraints. Moreover, these systems can benefit from application specific acceleration on data intensive workloads. For instance, Internet of Things (IoT) devices are tasked with acquiring, storing, and processing vast amounts of acquired information. Edge systems may slightly relax power/energy constraints, but can benefit from acceleration of machine learning, security, or other application specific tasks while maintaining quality of service on tasks from simultaneous disparate users. This project explores applying a new and understudied emerging memory technology called domain-wall memory (DWM) and its application to the needs of mobile and edge devices. DWM has properties that can be exploited to increase storage density, access speed, and to relieve the memory access bottleneck that exists in modern systems. The PIs will leverage their expertise to create a cross-layer design approach spanning the device/circuit- through system-level to develop a novel cross-DWM (XDWM) memory architecture with lateral read and write access capabilities. These innovations will revolutionize storage and processing for next generation mobile and edge devices by providing synergistic data storage and efficient processing-in-memory (PIM) with hooks for reliability. A cross-layer evaluation methodology will be adopted to cover prototype fabrication, device-level characterization, architecture-level simulation, and full system integration and emulation to explore the PIM. The transformative nature of this research is a disruptive new memory system that is dense, reliable, energy-efficient, ultra low latency with compute capability that can revolutionize the storage and processing capabilities of next generation computing systems. Such systems particularly include IoT, mobile and secure shared use edge systems but also apply to high performance computing and cloud systems. Further impacts of the proposed research include the integration of various education and advocacy activities based on the resources available to the two PIs such as (i) outreach for local K-12 students through Pitt's “Investing Now” summer school and USF's “Engineering Day” and Expo, where Engineering solutions are showcased to approximately 10,000 K-12 students/parents/teachers. (ii) inclusivity: Both PIs have a track record of including Under-represented Minority (URM) students.. They will continue to focus on URM representation in their team. (iii) curriculum: course integration of the research at both sites.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

下一代移动系统需要具有前所未有的密度和访问速度的内存和存储，以符合严格的功率/能量和可靠性约束。此外，这些系统可以从数据密集型工作负载上的特定应用程序加速中受益。例如，物联网（IoT）设备的任务是获取，存储和处理大量获取的信息。边缘系统可能会稍微放松功率/能量限制，但可以从机器学习，安全性或其他特定于应用程序任务的加速度中受益，同时维持来自简单不同用户的任务的服务质量。该项目探讨了应用一种称为域壁存储器（DWM）的新的新兴内存技术及其在移动设备的需求中的应用。 DWM具有可以探索的属性，可以提高存储密度，访问速度，并挽救现代系统中存在的内存访问瓶颈。 PI将利用其专业知识来创建跨越设备/直通系统级别的跨层设计方法，以开发具有横向读取和写入访问功能的新颖的跨二字（XDWM）内存体系结构。这些创新将通过提供协同的数据存储和有效的内存处理（PIM）的挂钩来彻底改变下一代移动和边缘设备的存储和处理。将采用跨层评估方法来涵盖原型制造，设备级的特征，体系结构级模拟以及完整的系统集成和仿真，以探索PIM。这项研究的变革性质是一种破坏性的新存储系统，具有密集，可靠，节能，超低潜伏期，具有计算能力，可以彻底改变下一代计算系统的存储和处理能力。这样的系统特别包括物联网，移动和安全共享使用边缘系统，但也适用于高性能计算和云系统。拟议的研究的进一步影响包括基于两种PI的资源（例如（i）通过皮特的“投资现在的投资”暑期学校和USF的“工程日”和Expo来融合各种教育和倡导活动，例如（i）向当地的K-12学生推广，在其中，工程解决方案向大约10,000 k-12的K-12学生/父母/父母/老师/老师/老师/老师/老师/教师展示。 （ii）包容性：两个PI都有包括代表性不足的少数民族（URM）学生的记录。他们将​​继续专注于团队中的URM代表。 （iii）课程：两个站点研究的课程整合。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。

项目成果

Sustainable AI Processing at the Edge

边缘的可持续人工智能处理

• DOI: 10.1109/mm.2022.3220399
• 发表时间: 2023
• 期刊: IEEE Micro
• 影响因子: 3.6
• 作者: Ollivier, Sebastien;Li, Sheng;Tang, Yue;Cahoon, Stephen;Caginalp, Ryan;Chaudhuri, Chayanika;Zhou, Peipei;Tang, Xulong;Hu, Jingtong;Jones, Alex K.
• 通讯作者: Jones, Alex K.

Pod-racing: bulk-bitwise to floating-point compute in racetrack memory for machine learning at the edge

• DOI: 10.1109/mm.2022.3195761
• 发表时间: 2022-11
• 期刊: IEEE Micro
• 影响因子: 3.6
• 作者: S. Ollivier;Xinyi Zhang;Yue Tang;C. Choudhuri;Jingtong Hu;A.K. Jones

Toward Comprehensive Shifting Fault Tolerance for Domain-Wall Memories with PIETT

利用 PIETT 实现域壁存储器的全面移位容错

• DOI: 10.1109/tc.2022.3188206
• 发表时间: 2022
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Ollivier, Sebastien;Longofono, Stephen;Dutta, Prayash;Hu, Jingtong;Bhanja, Sanjukta;Jones, Alex K.
• 通讯作者: Jones, Alex K.