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

现代计算系统将通过利用对其环境的知识并基于大量感官流做出复杂的决定来实现情景感知。另一方面，在安全关键和高完整性系统中，检测和纠正违反定时和安全不变量的能力，即自我意识，是至关重要的。不幸的是，随着平台提高情景感知的复杂性增加，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，在情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烈自我意识的挑战。它通过定义一类新的软件形状(SOSH)平台来实现这一点，这些平台提供对硬件组件之间交换的数据流的直接控制。今天，SOSH平台可以使用现有的和商业上可用的硬件来实现，这些硬件包括传统的处理单元和可重编程的片上逻辑。SOSH数据流操作原语在可重新编程的硬件中构建，并插入传统的中央处理器、存储模块和I/O设备之间。通过将内存和I/O数据流转变为可管理的实体，解锁了一种新的自省程度，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范例进行工作负荷分析和预测、实施高级内存模型、识别和缓解安全威胁的研究途径。评估指标包括可实现的性能范围、编程接口的可表现性以及对机密数据访问和系统瓶颈的控制程度。SOSH组件定义中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、分析引擎和隐私强化的数据存储。所取得的成果将在同行评议的期刊、国际会议和研讨会上传播。此外，一组公开可用的代码、硬件设计和数据集存储库将在项目的整个生命周期内以及之后至少三年内进行维护。这项研究的目标是一种在复杂系统中实现强大自我意识的技术。关键的观察结果是，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，将数据流转变为可观察和可管理的实体，可以实现前所未有的自我意识。从这一观察结果出发，引入了一种新的软件形状(SOSH)平台范例。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包络，并指定在违反策略的情况下影响硬件和软件组件的操作。SOSH方法以集成嵌入式处理器和可编程逻辑的商业平台为目标。还利用对部分动态重新配置的支持来实现运行时自适应。考虑了数据流操作原语的四个超类。首先，合并原语允许定义连接来自不同组件的数据流的规则。接下来，重新排序/过滤原语适应流处理中的重要结果，以减少和重组组件之间移动的数据量。第三，分析/记录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，拆分原语允许有选择地重新路由子流，以改善定时并缓解性能瓶颈处的拥塞。SOSH范例为设计和分析高度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平台代表着一类新的真正自我评估和自我修改的系统的首次出现，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，软件可以系统地检查和影响硬件的行为；而硬件构建并利用应用程序的知识来实施目标感知管理。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.