CSR: Small: Cross-Layer Solutions Enabling Instant Computing for Edge Intelligence Devices

CSR：小：跨层解决方案为边缘智能设备提供即时计算

• 批准号: 2247156
• 负责人: Arman Roohi
• 金额: $ 53.39万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247156&HistoricalAwards=false
• 关键词: CSR; Small; Cross; Layer; Solutions

Cloud-based computation and communication encounter several severe challenges, including high latency, questionable scalability, quality of service, privacy, and security. These issues can be addressed by transitioning computing architecture from a traditional cloud-centric mindset to a thing-centric (data-centric) perspective. However, nearly 90% of the data generated by the Internet of Things (IoT) is not analyzed using the conventional mechanism, due to limited computing ability and constraints in power and area. Hence, resource-limited IoTs should be developed optimally to enhance overall performance and extend lifetime. Our objective in this project is to transition from the conventional paradigm to a Sense-Decide-Action mechanism, which can analyze data locally, autonomously, and sustainably instead of sending it to the cloud, thereby reducing the amount of communication via a new cross-layer co-design approach. The broader impact and significance of our project lie in systematically paving the way for innovative foundational computing schemes. These strategies enable efficient instant computing and the necessary design approaches for real-time processing and decision-making systems. This progress brings us closer to the reality of accommodating over a trillion interconnected devices and improving the data privacy of resource-limited IoTs for critical applications, including healthcare monitoring, automotive applications, and industrial sensing. The technical approach of this research revolves around the development of low-overhead strategies to accelerate edge intelligence sensory nodes' autonomous operation within an environment. This is achieved by (1) designing and analyzing non-von-Neumann architectures, co-integrating conversion and processing capabilities in conjunction with an unconventional number system to alleviate the existing data movement issue between off/on-chip and processor; and (2) implementing processing units for both generic computations and domain-specific and emerging applications. This automated process of architecture engineering creates search space, defines design strategy, and identifies the optimal architecture to improve metrics such as lifetime energy reduction and overall performance. We evaluate the functionalities and performance of the proposed Thrusts through extensive modeling and simulations, beginning from circuit-level design and progressing upwards. This research effectively establishes novel lightweight heterogeneous edge intelligence capable of autonomously processing various data and compute-intensive tasks in an energy-scarce environment, marking a significant step towards the efficient future of IoT applications.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.

基于云的计算和通信遇到了一些严重的挑战，包括高潜伏期，可疑的可伸缩性，服务质量，隐私和安全性。这些问题可以通过将计算体系结构从以云为中心的心态转变为以事物为中心（以数据为中心）的角度来解决。但是，由于计算能力有限和功率和区域的约束，因此未使用常规机制对物联网（IoT）生成的数据的近90％（IoT）进行分析。因此，应最佳地开发资源有限的物联网以提高整体性能并延长寿命。我们在该项目中的目标是从常规范式过渡到有感觉的驱动力机制，该机制可以在本地，自主和可持续地分析数据，而不是将其发送到云中，从而通过新的跨层共同设计方法减少通信的数量。我们项目的更广泛的影响和意义在于系统地为创新的基础计算方案铺平了道路。这些策略可实现有效的即时计算以及实时处理和决策系统的必要设计方法。这一进步使我们更接近适应数万亿个互连设备的现实，并改善了对关键应用程序（包括医疗保健监测，汽车应用程序和工业感应）的资源有限IoT的数据隐私。 这项研究的技术方法围绕着较低的策略的发展，以加速边缘智能感觉节点在环境中的自主操作。这是通过（1）设计和分析非枪neumann体系结构，与非常规的数字系统结合的协调转换和处理能力来实现的，以减轻片上/芯片和处理器之间现有的数据移动； （2）为通用计算以及域特异性和新兴应用程序实施处理单元。这种自动化的建筑工程过程创建了搜索空间，定义了设计策略，并确定了最佳体系结构，以改善诸如终身能源降低和整体性能之类的指标。我们通过广泛的建模和仿真来评估所提出的推力的功能和性能，从电路级设计和向上发展。 This research effectively establishes novel lightweight heterogeneous edge intelligence capable of autonomously processing various data and compute-intensive tasks in an energy-scarce environment, marking a significant step towards the efficient future of IoT applications.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.