POSE: Phase II: An Open Source Ecosystem for Collaborative Rapid Design of Edge AI Hardware Accelerators for Integrated Data Analysis and Discovery

这个项目为hls4ML开发了一个生态系统，这是一个在硬件上设计机器学习推理的工具。用流行语言(例如，PyTorch、Kera)编写的机器学习模型由hls4ML翻译成供数字电路设计者使用的专业描述。对于没有硬件设计专业知识的领域专家(例如科学家)来说，手工制作这些系统定义是一个门槛很高的过程，可能会导致低质量的结果。该项目将创新和部署基础设施和管理解决方案，为开发人员和用户提供支持，从而形成一个广泛、互联和得到良好支持的ML硬件设计自动化社区，将硬件专家和领域专家联系在一起。该项目将开发一套组件，包括hls4ml的支持基础设施。这些组成部分包括对用户的培训、对为该工具作出贡献的新组件的自动化测试和验证程序、对开发人员的安全验证、为该工具包的质量控制制定审查和审查程序，以及供用户和开发人员报告和请求功能的系统。该项目产生的生态系统将管理对预先设计和验证的软件包和知识产权硬件块目录的访问，这些软件包和知识产权硬件块既可用于教育目的，也可用于构建定制的机器学习计算系统。该生态系统将使来自广泛学科和附属机构(科学、卫生、移动、学术机构、政府实验室、工业)的应用和领域专家能够成功地利用自动化设计流程来创建定制的机器学习硬件。这将提高部署这些系统的基础科学发现和技术开发工作的生产率和整体能力。领域专家和硬件专家之间的协同作用将帮助这些社区创建强大的联合设计方法，并培训新一代专家，他们将熟练地将它们作为数据驱动学科中未来劳动力的一部分应用。这个合作项目汇集了来自西北大学、伊利诺伊大学(厄巴纳-香槟和芝加哥)和亚利桑那州立大学的研究人员。该项目的产品和活动将通过https://fastmachinelearning.org/hls4ml/提供。项目团队计划在项目完成后至少3年内维护项目库和网站。这一奖励反映了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。

This project develops an ecosystem for hls4ml, which is a tool for designing machine learning inference in hardware. Machine learning models written in popular languages (e.g. PyTorch, Keras) are translated by hls4ml into specialty descriptions utilized by digital circuit designers. Hand crafting these system definitions is a process with a high barrier to entry, likely to result in poor quality results, for domain experts (e.g., scientists) without hardware design expertise. This project will innovate and deploy infrastructures and management solutions for support of the developers and users, leading to an extensive, connected, and well-supported ML hardware design automation community bridging hardware experts and domain experts. The project will develop a set of components comprising a support infrastructure for hls4ml. These components include training for users, automated testing and validation procedures for new components contributed to the tool, security validation of developers, creation of review and vetting procedures for quality control of the tool set, and a system for users and developers to report and request features. The ecosystem resulting from this project will manage access to a catalog of pre-designed and validated software packages and Intellectual Property hardware blocks, which can be used for both educational purposes and to build custom machine learning computational systems. The ecosystem will enable application and domain experts from a wide range of disciplines and affiliations (science, health, mobile, academic institutions, government laboratories, industry) to successfully utilize automated design flows to create customized machine learning hardware. This will enhance the productivity and overall ability of the underlying science discovery and technology development efforts, where these systems are deployed. The synergy catalyzed between domain experts and hardware experts will help these communities create powerful co-design methodologies and train the new generation of experts who will be proficient in applying them as part of the future workforce in data-driven disciplines. This collaborative project brings together investigators from Northwestern University, University of Illinois (Urbana-Champaign and Chicago), and Arizona State University. The project’s products and activities will be made available through https://fastmachinelearning.org/hls4ml/ . The project team plans to maintain the project repositories and website for a minimum of 3 years past the completion of this project.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.