CSR: Small: ARTEMIS: Algorithm-Hardware Co-Design for Efficient Machine Learning Systems

随着部署在各种硬件系统上的机器学习算法的日益普及，在众多可能配置中识别最佳模型的问题引起了人们的极大关注。在给定的功率或延迟限制下，需要选择合适的平台来运行这些应用程序，这使得问题更加复杂。这种“硬件墙”迫使机器学习服务提供商不断重新设计底层硬件结构，以满足某些限制。该项目为机器学习算法和硬件平台的自动高效协同设计开发工具，这将大大减少机器学习系统的成本和上市时间。该项目为机器学习系统和算法-硬件平台协同设计引入了高效的元学习。具体而言，该项目将开发用于优化系统硬件约束下机器学习模型的元学习算法，并将高效机器学习系统的硬件设计作为机器学习问题本身，通过元学习优化算法有效解决。最后，该项目将开发多目标算法，用于协同设计机器学习应用程序和它们需要运行的硬件平台，并利用硬件工程和设计方案中的领域知识，以大大加速硬件感知模型优化。该项目的结果旨在改变高效机器学习系统的建模、优化和设计方法。此外，这项工作的目标是通过潜在地改变工程师如何以多学科的方式接受培训，以处理下一代技术进步，特别是有效和智能地共同设计机器学习算法及其运行的硬件平台的问题，从而具有重要的教育和指导成分。该项目将涉及各种各样的研究生和本科生学员，同时将项目的范围扩大到高中和中学生。本项目中开发的数据、代码、结果和模拟器将在整个项目期间以及项目结束后至少四年内公开提供。存储库的位置在卡内基梅隆大学能源意识计算小组的网站上（www.ece.cmu.edu/~enyac）.This)，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。

With the increased popularity of machine learning algorithms deployed on a variety of hardware systems, the problem of identifying the best model among numerous possible configurations has drawn significant attention. The problem is compounded by the need to select the right platform to run these applications, under given power or latency constraints. This "hardware wall" forces machine learning service providers to constantly redesign the underlying hardware fabric to satisfy certain constraints. This project develops tools for automatic and efficient co-design of machine learning algorithms and hardware platforms that will result in significant cost and time-to-market reduction for machine learning systems.The project introduces efficient meta-learning for machine learning systems and algorithm-hardware platform co-design. Specifically, the project will develop meta-learning algorithms for the optimization of machine learning models under system hardware constraints and formulate the hardware design of efficient machine learning systems as a machine learning problem itself, that can be effectively solved by meta-learning optimization algorithms. Finally, the project will develop multi-objective algorithms for the co-design of machine learning applications and hardware platforms they need to run on, and exploit domain knowledge from hardware engineering and design schemes to substantially accelerate hardware-aware model optimization.The results of the project seek to change the landscape of modeling, optimization, and design methodologies for efficient machine learning systems. Furthermore, the work aims to have an important educational and mentoring component by potentially changing how engineers are trained in a multidisciplinary fashion for dealing with next generation technological advances in general, and the problem of efficiently and intelligently co-designing machine learning algorithms and the hardware platforms they are running on, in particular. The project will involve a diverse graduate and undergraduate trainee population, while expanding the project's outreach to high-school and middle-school students.The data, code, results, and simulators developed in this project will be made available publicly throughout the duration of the project and for at least four years after the end of the project. The location of the repository is on the website of Carnegie Mellon University's Energy Aware Computing group (www.ece.cmu.edu/~enyac).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.