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）加速器是为深度学习算法等计算密集型应用而设计的硬件。随着人工智能（AI）和机器学习在当今许多电子系统中的使用，DNN加速器正迅速成为常见的地方。其中一些包括安全关键应用，如汽车，医疗保健和航空航天。然而，许多高精度DNN加速器消耗大量能量，使得它们的使用受限于能量受限的设备。这个问题的一个流行的解决方案是硬件近似，其中加速器的近似设计被认为是足够的能量效率的目的。与精确的结果相比，使用硬件近似可以使结果更容易受到永久性故障的影响。永久性故障通常导致在制造后阶段丢弃。这种永久性故障也可能出现在芯片使用的部署后阶段。这两种情况都是环境代价高昂且不可持续的。本研究的主要目标是为近似深度神经网络（DNN）开发一种新的可持续性感知设计流程，该设计流程可以通过重用和自我修复来延长其寿命，同时优化性能和可持续性指标。维修能力允许它们的重复使用和重新使用，其操作能力接近原始基线精度。基于这一目标，该项目旨在改变设计和部署AxDNN的最新技术，提出了三个研究目标：（i）在新颖高效的神经结构搜索方法的帮助下，研究设计可靠和可持续的AxDNN的方法;（ii）在旁路电路、近似再训练的帮助下，研究AxDNN中制造后和部署后故障缓解的方法，混合内建自测试和通过权重交换进行自修复;以及（iii）开发仿真和现场可编程门阵列（FPGA）演示平台，以评估和演示与用户定义的一阶度量和新颖的可持续性感知维度相比所产生的AxDNN的有效性。项目成果（即，新的理论、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向更广泛的机器学习和网络物理系统（CPS）社区公开提供。此外，项目成果将为计算机和电气工程本科生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的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。