CAREER: Scalable and Adaptable Sparsity-driven Methods for more Efficient AI Systems

人工智能(AI)，尤其是深度神经网络(DNN)在涉及图像、自然语言处理和蛋白质结构等许多认知任务上取得了比人类更高的准确性。不幸的是，由于高数据处理要求，人工智能系统通常运行在耗电的专用计算硬件上。量化，或接近较小的数值，已经被用来减少计算要求。然而，固定的低位宽DNN可能会由于量化误差而在精度上遭受损失。许多现有的量化软件解决方案在位宽选择上也是固定的或受限的。为了解决这种权衡并利用数据稀疏性，研究团队将研究最先进的方法，并开发新的数据量化、编码和压缩算法，以与现有的人工智能系统集成。所开发的方法不仅有可能提高性能，还有可能降低人工智能系统的功率需求并提高能效。它们将在小型设备上启用DNN推理等AI应用，从而减轻云基础设施的负载，改善用户体验，提供数据隐私，避免安全风险。该项目提出的工作有可能突破许多人工智能应用程序的界限，这些应用程序运行在能源存储受限的设备上，如智能传感、可穿戴设备和自动驾驶。研究和教育工具将促进和增加学生和研究社区在推进人工智能研究方面的参与。这项工作将在一家为少数群体服务的机构进行，资金将支持来自代表性不足群体的学生。该项目的研究目标是研究能够利用稀疏性并提高人工智能系统效率的量化和压缩方法。首席调查员(PI)计划研究自适应量化和压缩方法，以利用人工智能系统中的稀疏性，同时在非稀疏情况下将开销降至最低，并将精度损失降至最低。将研究和定义所建议方法的精度和性能之间的权衡，以便自动调整精度、性能或能效的优先级。PI计划开发一个原型，并行执行建议的方法，使建议的方法真正有效地适用于数据中心和先进的硬件架构。建议的方法将被打包到一个AI向量基元库中，该库将与几个流行的深度学习框架集成，作为概念证明，主要针对GPU和CPU系统。将为像Pytorch或TensorFlow这样的框架开发一个集成API，以允许与其他向量原语轻松集成。软件库将与具有自动反馈和激励环境的基于网络的学习平台相集成，以鼓励学生参与解决人工智能挑战。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。

Artificial Intelligence (AI) and, in particular, Deep Neural Networks (DNN) have achieved better than human accuracy on many cognitive tasks involving images, natural language processing, and protein structure, among others. Unfortunately, due to high data processing demands, AI systems are typically run on power-hungry specialized computing hardware. Quantization, or approximation to smaller numerical values, has been used to reduce computing requirements. However, the fixed low bit-width DNNs may suffer losses in accuracy due to quantization errors. Many existing software solutions for quantization are also fixed or limited in bit-width choices. To address this trade-off and leverage data sparsity, the research team will investigate state-of-the-art methods and develop novel data quantization, encoding, and compression algorithms to integrate with existing AI systems. The methods developed have the potential to not only improve performance but also to reduce power requirements and boost the energy efficiency of AI systems. They will enable AI applications such as DNN inference on small devices, thus reducing the load on cloud infrastructure, improving user experience, providing data privacy, and avoiding security risks. The work proposed in this project has the potential to push the boundaries in many AI applications that run on energy storage-constrained devices, such as smart sensing, wearable devices, and autonomous driving. The research and educational tools will facilitate and increase student and research community participation in advancing AI research. The work will be conducted at a minority-serving institution, and the funding will support students from underrepresented groups.The research goal of this project is to investigate quantization and compression methods that can leverage sparsity and improve efficiency in AI systems. The principal investigator (PI) plans to study adaptable quantization and compression methods to leverage sparsity in AI systems while minimizing the overhead in non-sparse situations and minimizing accuracy loss. The trade-off between accuracy and performance with the proposed methods will be studied and defined for automated tunable prioritization of either accuracy, performance, or energy efficiency. The PI plans to develop a prototype with parallel execution of the proposed methods to make the proposed methods truly effective for data centers and advanced hardware architectures. The proposed methods will be packaged into an AI vector primitives library that will be integrated with several popular Deep Learning frameworks as proof of concept, primarily targeting GPU and CPU systems. An integration API will be developed for frameworks like Pytorch or TensorFlow to allow easy integration with other vector primitives. Software libraries will be integrated with a web-based learning platform with automated feedback and a motivating environment to encourage student participation in solving AI challenges.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.