Collaborative Research: DESC: Type I: SEEDED: Sustainability-aware Reliable and Reusable AI Hardware Design

合作研究：DESC：类型 I：SEEDED：具有可持续性意识的可靠且可重复使用的人工智能硬件设计

• 批准号: 2323820
• 负责人: Benjamin Carrion Schaefer
• 金额: $ 25.97万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323820&HistoricalAwards=false
• 关键词: Collaborative; Research; DESC; Type; SEEDED

Deep Neural Network (DNN) accelerators are hardware designed for compute-intensive applications like deep learning algorithms. DNN accelerators are quickly becoming common place as artificial intelligence (AI) and machine learning are used in many electronic systems today. Some of these include safety-critical applications such as automotive, healthcare, and aerospace. However, many of the high-precision DNN accelerators consume a lot of energy, making their use limited for energy-constrained devices. A popular solution to this issue has been hardware approximation, where an approximate design of the accelerator is considered to be sufficient for purposes of energy efficiency. The use of hardware approximation can make the outcome up to 3X more vulnerable to permanent faults compared to their accurate counterparts. Permanent faults usually lead to discarding in the post-fabrication phase. Such permanent faults can also appear in the post-deployment phase when the chip is in use. Both cases are environmentally costly and not sustainable. The main objective of this research is to develop a new sustainability-aware design flow for approximate deep neural networks - DNNs that can prolong their lifetime by enabling reuse and self-repair while also optimizing performance and sustainability metrics.A sustainability-aware design flow in the early design phases and augmenting approximate hardware-based DNN accelerators (AxDNNs) with lightweight fault detection and self-repair capability allows their reuse and re-purpose with operating capability close to the original baseline accuracy. Motivated by this goal, this project aims to transform the state-of-the-art in designing and deploying AxDNNs with three proposed research objectives: (i) investigating methods for designing reliable and sustainable AxDNNs with the help of a novel and efficient neural architecture search methods; (ii) investigating methods for post-fabrication and post-deployment fault mitigation in AxDNNs with the help of bypass circuitry, approximate retraining, hybrid built-in-self-test, and self-repair through weight swapping; and, (iii) developing a simulation and field programmable gate arrays (FPGAs) demonstration platform to evaluate and demonstrate the effectiveness of the resulting AxDNNs compared against user-defined first-order metrics and novel sustainability-aware dimensions. The project outcomes (i.e., new theories, tools, codes, benchmarks, and case studies) will be publicly made available to the broader machine learning and cyber-physical system (CPS) communities through open-source software and peer-reviewed publications. In addition, the project outcomes will create a new curriculum and hands-on laboratory exercises for computer and electrical engineering undergraduate and graduate courses.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.

深度神经网络（DNN）加速器是用于计算密集型应用程序（例如深度学习算法）的硬件。随着人工智能（AI）和机器学习在当今许多电子系统中使用，DNN加速器正迅速成为普遍的位置。其中一些包括安全至关重要的应用，例如汽车，医疗保健和航空航天。但是，许多高精度DNN加速器都会消耗大量能量，从而限制了用于能源受限的设备的限制。对此问题的流行解决方案是硬件近似，在该问题中，加速器的近似设计被认为足以满足能源效率的目的。与准确的同行相比，硬件近似的使用可以使结果更容易受到永久性故障的影响。永久性断层通常会导致在制作后阶段丢弃。当使用芯片时，这种永久性的断层也可能出现在剥离后阶段。这两个案件在环境上都是昂贵的，也不可持续。这项研究的主要目的是开发一种新的可持续性意识到的设计流，以近似深层的神经网络 - DNN可以通过启用重复使用和自我修复来延长其寿命，同时还可以优化性能和可持续性度量。重新使用，其操作能力接近原始基线精度。在这个目标的推动下，该项目旨在通过提出的三个研究目标来改变设计和部署AXDNN的最新技术：（i）研究在一种新颖，高效的神经建筑搜索方法的帮助下，研究用于设计可靠且可持续的AXDNN的方法； （ii）研究借助旁路电路，近似重试，杂交内置测试以及通过重量交换来调查AXDNNS进行后制作和部署后断层缓解方法； （iii）开发一个模拟和现场可编程栅极阵列（FPGA）演示平台，以评估和演示所得的AXDNN与用户定义的一阶指标和新颖的可持续性感知尺寸相比。项目结果（即新理论，工具，代码，基准和案例研究）将通过开源软件和同行评审的出版物公开提供给更广泛的机器学习和网络物理系统（CPS）社区。此外，该项目的成果将为计算机和电气工程本科和研究生课程创建新的课程和动手实验室练习。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来评估的。