SHF: Small: Beyond Accelerators - Using FPGAs to Achieve Fine-grained Control of Data-flows in Embedded SoCs

SHF：小型：超越加速器 - 使用 FPGA 实现嵌入式 SoC 中数据流的细粒度控制

• 批准号: 2008799
• 负责人: Renato Mancuso
• 金额: $ 49.99万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008799&HistoricalAwards=false
• 关键词: SHF; Small; Beyond; Accelerators; Using

Modern computing systems are to become context-aware by exploiting knowledge of their environment and taking complex decisions based on a multitude of sensory streams. On the other hand, in safety-critical and high-integrity systems, the capability to detect and correct violations of timing and security invariants, i.e., self-awareness, is of the utmost importance. Unfortunately, as platforms grow in complexity to improve context-awareness, the inter-play between concurrent software components and the underlying hardware becomes hard to predict and to reason about. Therefore, there exists a fundamental tension between context- and self-awareness. This research tackles the challenge of achieving strong self-awareness without trading off system complexity. It does so by defining a new class of software-shaped (SOSH) platforms that provide direct control over the flow of data exchanged between hardware components. SOSH platforms can be implemented today using existing and commercially available hardware that includes traditional processing units and reprogrammable logic on-chip. SOSH data-flow manipulation primitives are constructed in reprogrammable hardware and interposed between traditional central processors, memory modules, and I/O devices. By turning memory and I/O data-flows into manageable entities, a new degree of introspection is unlocked, which constitutes the premise for self-awareness. The project explores key design principles in the definition and implementation of low-overhead SOSH primitives for operations over data-flows. It investigates research avenues on the use of the SOSH paradigm to enact workload profiling and prediction; to implement advanced memory models; to perform security threat identification and mitigation. Evaluation metrics include achievable performance envelopes, expressiveness of programming interfaces, and level of control over access to confidential data and system bottlenecks. The milestones achieved in the definition of SOSH components will be immediately transitioned into practice. Areas of impact include, but are not limited to, civil avionics, autonomous driving technology, analytics engines, and privacy-hardened data stores. The obtained results will be disseminated in peer-reviewed journals, international conferences, and workshops. In addition, a set of publicly available repositories of code, hardware designs, and datasets will be maintained throughout the lifespan of the project and for a minimum of three years thereafter.The goal of this research is a technology to achieve strong self-awareness in complex systems. The key observation is that the interplay between software and hardware modules (i.e., CPUs, GPUs, DSPs, memory modules, and I/O devices) is embedded in the flow of data they exchange. Thus, turning data-flows into observable and manageable entities enables an unprecedented degree of self-awareness. From this observation, a new paradigm for software-shaped (SOSH) platforms is introduced. In SOSH platforms, the software can instantiate hardware modules to constantly monitor data-flows. It can define policies and performance envelopes for data-flow exchanges, and specify actions that affect both hardware and software components in case of a policy violation. The SOSH methodology targets commercial platforms that integrate embedded processors and programmable logic. Support for partial dynamic reconfiguration is also leveraged to achieve runtime adaptation. Four super-classes of data-flow manipulation primitives are considered. First, merging primitives enable the definition of rules to join data-flows from different components. Next, reordering/filtering primitives adapt seminal results in stream-processing to reduce and reorganize the amount of data moved between components. Third, profiling/logging primitives support the extraction of data-flow characteristics for prediction and state/progress tracking of application workloads. Lastly, splitting primitives allow selective re-routing of sub-flows to improve timing and relieve congestion at the performance bottlenecks. The SOSH paradigm lays the basis for novel approaches for the design and analysis of high-integrity and safety-critical systems. More broadly, SOSH platforms represent the first appearance of a new class of truly self-assessing and self-modifying systems, shaking the foundations of traditional hardware/software layers as non-participating entities. In SOSH platforms, the software can systematically inspect and influence the behavior of the hardware; while the hardware constructs and leverages knowledge of applications to enact goal-aware management.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.

现代计算系统将通过利用环境知识和基于大量感觉流做出复杂决策来实现上下文感知。另一方面，在安全关键型和高完整性系统中，检测和纠正违反时间和安全不变量的能力，即自我意识，是至关重要的。不幸的是，随着平台为了提高上下文感知能力而变得越来越复杂，并发软件组件和底层硬件之间的相互作用变得难以预测和推理。因此，语境意识和自我意识之间存在着根本的张力。这项研究解决了在不牺牲系统复杂性的情况下实现强自我意识的挑战。它通过定义一类新的软件型（SOSH）平台来实现这一点，这些平台提供了对硬件组件之间交换的数据流的直接控制。SOSH平台可以使用现有的和商业上可用的硬件来实现，包括传统的处理单元和芯片上的可编程逻辑。SOSH数据流操作原语在可重新编程的硬件中构造，并插入到传统的中央处理器、内存模块和I/O设备之间。通过将内存和I/O数据流转换为可管理的实体，可以解锁一种新的自省程度，这构成了自我意识的前提。该项目探讨了数据流操作的低开销SOSH原语的定义和实现中的关键设计原则。它调查了使用SOSH范式制定工作负载分析和预测的研究途径；实现高级内存模型；执行安全威胁识别和缓解。评估指标包括可实现的性能信封、编程接口的表达性，以及对机密数据和系统瓶颈的访问控制级别。在定义SOSH组件方面取得的里程碑将立即转化为实践。受影响的领域包括但不限于民用航空电子设备、自动驾驶技术、分析引擎和加强隐私保护的数据存储。获得的结果将在同行评议的期刊、国际会议和讲习班上传播。此外，一组公开可用的代码存储库、硬件设计和数据集将在项目的整个生命周期内以及之后的至少三年时间内得到维护。本研究的目标是在复杂系统中实现强自我意识的技术。关键的观察是，软件和硬件模块（即cpu、gpu、dsp、内存模块和I/O设备）之间的相互作用嵌入在它们交换的数据流中。因此，将数据流转化为可观察和可管理的实体，可以实现前所未有的自我意识。根据这一观察，本文介绍了一种软件型（SOSH）平台的新范式。在SOSH平台中，软件可以实例化硬件模块以持续监控数据流。它可以为数据流交换定义策略和性能信封，并指定在违反策略时影响硬件和软件组件的操作。SOSH方法的目标是集成嵌入式处理器和可编程逻辑的商业平台。还利用对部分动态重新配置的支持来实现运行时适应。考虑了数据流操作原语的四个超类。首先，合并原语允许定义规则来连接来自不同组件的数据流。接下来，重新排序/过滤原语适应流处理中的重要结果，以减少和重新组织组件之间移动的数据量。第三，分析/日志原语支持提取数据流特征，用于预测和跟踪应用程序工作负载的状态/进度。最后，拆分原语允许选择性地重路由子流，以改善时间并缓解性能瓶颈处的拥塞。SOSH范式为设计和分析高完整性和安全关键系统的新方法奠定了基础。更广泛地说，SOSH平台代表了一类真正自我评估和自我修改的新系统的首次出现，动摇了传统硬件/软件层作为非参与实体的基础。在SOSH平台中，软件可以系统地检测和影响硬件的行为；而硬件构造和利用应用程序的知识来实施目标感知管理。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

E-WarP: A System-wide Framework for Memory Bandwidth Profiling and Management

E-WarP：用于内存带宽分析和管理的系统范围框架

• DOI: 10.1109/rtss49844.2020.00039
• 发表时间: 2020
• 期刊: 41st IEEE Real-Time Systems Symposium (RTSS 2020
• 作者: Sohal, Parul;Tabish, Rohan;Drepper, Ulrich;Mancuso, Renato
• 通讯作者: Mancuso, Renato

Observing the Invisible: Live Cache Inspection for High-Performance Embedded Systems

• DOI: 10.1109/tc.2021.3060650
• 发表时间: 2020-07
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Dharmesh Tarapore;Shahin Roozkhosh;S. Brzozowski;R. Mancuso

Relational Memory: Native In-Memory Accesses on Rows and Columns

• DOI: 10.48786/edbt.2023.06
• 发表时间: 2021-09
• 期刊: Current opinion in molecular therapeutics
• 作者: Shahin Roozkhosh;Denis Hoornaert;J. Mun;Tarikul Islam Papon;Ulrich Drepper;R. Mancuso;Manos Athanassoulis

Lazy Load Scheduling for Mixed-criticality Applications in Heterogeneous MPSoCs

异构 MPSoC 中混合关键性应用的延迟加载调度

• DOI: 10.1145/3587694
• 发表时间: 2023
• 期刊: ACM Transactions on Embedded Computing Systems
• 影响因子: 2
• 作者: Kloda, Tomasz;Gracioli, Giovani;Tabish, Rohan;Mirosanlou, Reza;Mancuso, Renato;Pellizzoni, Rodolfo;Caccamo, Marco
• 通讯作者: Caccamo, Marco

Software-Shaped Platforms

软件型平台

• DOI: 10.1145/3576914.3587546
• 发表时间: 2023
• 期刊: 2nd Workshop on Real-time Intelligent Edge Computing (RAGE'23
• 作者: Mancuso, Renato;Roozkhosh, Shahin;Hoornaert, Denis;Mun, Ju Hyoung;Papon, Tarikul Islam;Athanassoulis, Manos
• 通讯作者: Athanassoulis, Manos