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，以满足严格的功率/能源和可靠性限制。此外，这些系统可以受益于数据密集型工作负载的特定应用加速。负责获取、存储和处理大量获取信息的边缘系统可能会稍微放松功率/能源限制，但可以受益于机器学习、安全或其他特定于应用程序的任务的加速，同时保持同时不同任务的服务质量。该项目探索应用新的和正在研究称为域壁内存 (DWM) 的新兴内存技术及其在移动和边缘设备需求中的应用 DWM 具有可用于提高存储密度、访问速度并缓解现代内存访问瓶颈的特性。 PI 将利用他们的专业知识创建跨越器件/电路到系统级的跨层设计方法，以开发具有横向读写访问功能的新型跨 DWM (XDWM) 存储器架构。存储和通过提供协同数据存储和高效的内存处理（PIM）以及可靠性钩子，将采用跨层评估方法来覆盖原型制造、设备级表征、架构级，从而为下一代移动和边缘设备提供处理。这项研究的变革本质是一种颠覆性的新型存储系统，它具有密集、可靠、节能、超低延迟的计算能力，可以彻底改变计算机的存储和处理能力。下一代计算系统。此类系统特别包括物联网、移动和安全共享边缘系统，但也适用于高性能计算和云系统。拟议研究的进一步影响包括基于两个 PI 可用资源的各种教育和宣传活动的整合。 (i) 通过皮特的“立即投资”暑期学校和南佛罗里达大学的“工程日”和博览会，向当地 K-12 学生进行宣传，向大约 10,000 名 K-12 学生/家长/教师展示工程解决方案。 (ii) 包容性：两位 PI 都有包容少数族裔 (URM) 学生的记录。他们将​​继续关注团队中的 URM 代表性。(iii) 课程：两个地点研究的课程整合。授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。

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: 2023-01
• 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.

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: 2022-07
• 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.

POD-RACING: Bulk-Bitwise to Floating-Point Compute in Racetrack Memory for Machine Learning At the Edge

POD-RACING：赛道内存中的批量按位到浮点计算，用于边缘机器学习

• DOI: 10.1109/mm.2022.3195761
• 发表时间: 2022-01
• 期刊: IEEE Micro
• 影响因子: 3.6
• 作者: Ollivier, Sebastien;Zhang, Xinyi;Tang, Yue;Choudhuri, Chayanika;Hu, Jingtong;Jones, Alex K.
• 通讯作者: Jones, Alex K.