CAREER: An Agile Compiler Framework for Spatial Dataflow Accelerators

在大型机器学习模型时代，硬件加速器在实现快速和可扩展的训练和推理性能方面发挥着关键作用。其中，空间自适应加速器（SDA），如张量处理单元，在加速要求苛刻的神经网络工作负载方面非常成功。随后，许多不同的加速器平台已经引入了高度可编程的接口。尽管在硬件方面取得了这些进步，但编译器一直落后，对这些加速器的支持有限。当前手动构建编译器后端的方法对于各种快速发展的加速器来说是不可持续的。该项目的新奇在于一套自动化和参数化的编译器构造方法，可以生成针对各种空间并行加速器设计的优化代码。该项目的影响是使硬件设计人员能够快速构建用于新型新兴架构的优化编译器，这反过来又将使新硬件平台的使用民主化，以加速各种机器学习工作负载。研究者的综合教育计划创建了一个新颖的机器学习编译器课程，该课程集成了本项目中探索的编译器主题的形式主义，编译技术和机器学习。这门新课程为学生提供了必要的知识，以便在涉及为硬件加速的机器学习工作负载设计和维护编译器的职业生涯中取得成功。研究者计划通过公开竞赛开源代码和数据，发表学术论文，与主要的行业合作伙伴合作进行技术转让，举办学术研讨会，并增加大学生的参与，以扩大拟议研究活动的影响。该项目探索适合新兴SDA的新型自动编译器构建方法。与现有的商品硬件平台相比，新兴的软件开发协议更加多样化，具有更快的设计迭代周期，并且具有昂贵的执行方式。该项目研究了新技术，通过在形式化方法和机器学习前沿进行协同创新，解决后端代码生成的三个不同方面，以满足上述SDA的特征。首先，它开发参数表示和形式化的张量编译器中间表示（IR）和SDA描述。它使用这些来自动生成专用于每个SDA的代码生成器。其次，该项目开发了创新的解决方案，需要更少的目标数据来从成熟的加速器中转移学习的成本模型。最后，该项目探索了新颖的多保真度优化技术，这些技术利用不同的执行方式在昂贵的模拟中找到更快的自动调整解决方案。该项目的成功完成产生了一个敏捷的编译器框架，可以快速生成可重定向的编译器后端新兴的空间低速accelerators.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

In the era of large machine learning models, hardware accelerators play a key role in achieving fast and scalable training and inference performance. Among them, spatial dataflow accelerators (SDA), such as tensor processing units, have been extremely successful at accelerating demanding neural network workloads. Subsequently, many diverse accelerator platforms have been introduced with heavily programmable interfaces. In spite of these advances in hardware, compilers have been lagging behind, having only limited support for these accelerators. The current approach of manually constructing compiler backends is not sustainable with diverse and rapidly evolving accelerators. The project’s novelty is a set of automated and parameterized compiler construction methodologies that can generate optimized code targeting a wide array of spatial dataflow accelerator designs. The project’s impact is enabling hardware designers to rapidly build optimizing compilers for novel emerging architectures, which in turn will democratize the usage of new hardware platforms for accelerating diverse machine learning workloads. The investigator’s integrated education plan creates a novel machine learning compilers course that integrates formalisms, compilation techniques, and machine learning for compilers topics explored in this project. This new course offering prepares students with the necessary knowledge to succeed in careers that involve designing and maintaining compilers for hardware-accelerated machine learning workloads. The investigator plans to open source both the code and the data with public competitions, publish academic papers, collaborate with key industry partners with the possibility of technology transfers, hold academic workshops, and increase undergraduate participation to broaden the impact of the proposed research activities.The project explores novel automated compiler construction methodologies that are suitable for emerging SDAs. Compared to established commodity hardware platforms, emerging SDAs are more diverse, have faster design iteration cycles, and have expensive execution modalities. The project investigates novel techniques tackling three different aspects of backend code generation by synergistically innovating in both formal methods and machine learning fronts that can cater to the aforementioned characteristics of SDAs. First, it develops parametric representations and formalisms of tensor compiler intermediate representations (IR) and SDA descriptions. It uses these to automatically generate code generators specialized to each SDA. Second, the project develops innovative solutions that require significantly less target data to transfer learned cost models from mature accelerators. Finally, the project explores novel multi-fidelity optimization techniques that leverage different execution modalities to find faster auto-tuning solutions amidst expensive simulations. Successful completion of the project produces an agile compiler framework that can rapidly generate retargetable compiler backends for emerging spatial dataflow accelerators.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.