CAREER: A Framework for Co-design and Optimization of Programmable Hardware Accelerators and Compilers

创造节能的计算系统对于实现可持续发展的社会目标至关重要。随着摩尔定律所预测的半导体技术规模放缓，特定领域的硬件加速器，即专门用于完成某些任务的硬件块，将在提高计算系统的性能和能效方面发挥越来越重要的作用。现代移动芯片具有数十个加速器，用于图像处理，视频编码，图形，神经网络等应用，以实现低功耗和快速处理速度。然而，随着机器学习的进步，这些应用正在快速变化。保持高性能和高能效需要硬件加速器、编译器和应用程序同步发展。不幸的是，实现这一目标的现有方法涉及大量的手工工作。大型工程团队详细研究加速器体系结构，并以特别的方式修改编译器，利用低级库来针对特定的硬件特性。由于维护软件堆栈的开销很大，因此随着新领域或现有领域的发展，加速它们仍然具有挑战性。我们需要的是一种结构化的方法，用于生成可编程加速器，并在加速器体系结构随着应用程序的发展而发展时更新软件编译器。这个项目提出了一个设计空间探索和优化框架，它自动生成加速器架构，接近手工设计的效率，大大降低了硬件和编译器生成的设计工作量。通过缩短产品上市时间和创建更高效的设计团队，这项工作可以影响当今行业中硬件软件系统设计的完成方式。此外，作为这项工作的一部分开发的公开共享课程将确保公平获得教育机会，并有助于创建多元化，具有全球竞争力的半导体劳动力。该项目的研究目标是为特定领域的硬件加速器和编译器的自动协同设计和优化创建一个框架。该框架将有三个组成部分：(1)基于频繁子图挖掘和合并的自动加速器处理元素（PE）设计空间优化工具，(2)用于密集和稀疏应用程序的加速器内存元素优化工具，以及(3)用于自动确定应用程序到加速器的最佳映射的自动调度程序。这些工具将用于在硅中设计、优化和原型化统一可编程加速器，用于图像处理和机器学习等密集应用领域和图形分析等稀疏应用领域，并演示显著优于通用架构和接近特定应用集成电路的能效和性能指标。所提出的方法使用了几种不同于先前工作的技术来实现自动加速器-编译器协同设计和优化。首先，它允许硬件规范中的任何更改自动传播到编译器中，而无需手动操作。这种独特的性质是实现加速器大规模设计空间探索的关键。如果没有它，就必须在每次硬件更改时手动更新应用程序编译器，这极大地限制了可以探索的设计点的数量。其次，提出的用于加速器的自动PE优化框架使用频繁的子图挖掘和合并，从应用程序图本身生成高效的PE架构。这种方法与以前的工作完全不同，以前的工作不执行应用程序驱动的优化，而是搜索许多可能的PE参数值。因此，与以前的工作相比，这种方法有望具有更高的样本效率和更快的速度。最后，与现有的用于可编程加速器的商业高级合成工具和编译器不同，这些工具和编译器要求用户在应用程序代码中提供低级调度指令，所提出的自动调度程序将自动搜索应用程序到可编程加速器的最佳映射，极大地提高了用户的工作效率。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。

Creating energy-efficient computing systems is essential for achieving the societal goal of sustainability. As the semiconductor technology scaling predicted by Moore’s law slows down, domain-specific hardware accelerators, i.e., hardware blocks specialized to do certain tasks very well, will play an increasingly important role in improving the performance and energy-efficiency of computing systems. Modern mobile chips have dozens of accelerators for applications such as image processing, video coding, graphics, neural networks etc. to achieve low power consumption and fast processing speeds. However, with advances in machine learning, these applications are changing at a rapid pace. Maintaining high performance and energy-efficiency requires that hardware accelerators, compilers, and applications evolve together in lockstep. Unfortunately, existing methodologies to achieve this involve significant manual effort. Large engineering teams study the accelerator architecture in detail and modify the compiler in an ad hoc manner, leveraging low-level libraries to target specific hardware features. Because of the large overhead in maintaining the software stack, it remains challenging to accelerate new domains or existing domains as they evolve. What is needed is a structured approach for generating programmable accelerators and for updating the software compiler as the accelerator architecture evolves with the applications. This project proposes a design-space exploration and optimization framework that automatically generates accelerator architectures that approach the efficiencies of hand-designed ones, with a significantly lower design effort for both hardware and compiler generation. This work can impact how hardware-software system design is done today in the industry, by reducing the time to market for products and creating more productive design teams. Moreover, the openly shared curriculum developed as a part of this work will ensure equitable access to educational opportunities and help create a diverse, globally competitive semiconductor workforce. The research goal of this project is to create a framework for automated co-design and optimization of domain-specific hardware accelerators and compilers. The framework will have three components: (1) an automated accelerator processing element (PE) design space optimization tool based on frequent subgraph mining and merging, (2) an accelerator memory element optimization tool for both dense and sparse applications, and (3) an auto-scheduler for automatically determining the best mapping of an application to the accelerator. These tools will be used to design, optimize and prototype in silicon a unified programmable accelerator for both dense application domains such as image processing and machine learning and sparse application domains such as graph analytics, and demonstrate energy-efficiency and performance metrics that significantly beat general purpose architectures and approach application-specific integrated circuits. The proposed approach uses several techniques, distinct from prior work, to achieve automatic accelerator-compiler co-design and optimization. First, it allows any change in the hardware specification to automatically propagate into the compiler with no manual effort. This unique property is the key to enabling large-scale design space exploration of accelerators. Without it, one would have to manually update the application compiler with every hardware change, greatly limiting the number of design points one can explore. Second, the proposed framework for automated PE optimization for accelerators generates efficient PE architectures from the application graphs themselves, using frequent subgraph mining and merging. This approach is quite different from prior work, which does not perform application-driven optimization but rather searches over many possible PE parameter values. As a result, this approach promises to be much more sample-efficient and faster versus prior work. Finally, as opposed to existing commercial high level synthesis tools and compilers for programmable accelerators which require the user to provide low-level scheduling directives in the application code, the auto-scheduler proposed will automatically search for the best mapping of an application to the programmable accelerator, greatly improving user productivity.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.