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

基于云的计算和通信面临着一些严峻的挑战，包括高延迟、可扩展性、服务质量、隐私和安全性。这些问题可以通过将计算架构从传统的以云为中心的思维方式转变为以事物为中心（以数据为中心）的观点来解决。然而，由于有限的计算能力以及功率和面积的限制，物联网（IoT）产生的近90%的数据没有使用传统机制进行分析。因此，资源有限的物联网应该以最佳方式开发，以提高整体性能并延长寿命。我们在这个项目中的目标是从传统的范式过渡到感知-决定-行动机制，该机制可以本地，自主和可持续地分析数据，而不是将其发送到云端，从而通过新的跨层协同设计方法减少通信量。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道路。这些策略使高效的即时计算和必要的设计方法，实时处理和决策系统。这一进展使我们更接近于容纳超过万亿个互连设备的现实，并改善了资源有限的物联网关键应用的数据隐私，包括医疗监控，汽车应用和工业传感。 本研究的技术方法围绕开发低开销策略来加速边缘智能感知节点在环境中的自主操作。这是通过以下方式实现的：（1）设计和分析非冯·诺依曼架构，将转换和处理能力与非传统的数字系统结合起来，以减轻片外/片上和处理器之间现有的数据移动问题;以及（2）为通用计算和特定领域和新兴应用实现处理单元。这种架构工程的自动化过程创建搜索空间，定义设计策略，并确定最佳架构，以改善诸如寿命能耗降低和整体性能等指标。我们通过广泛的建模和仿真，从电路级设计开始，并向上发展，评估拟议的推力的功能和性能。这项研究有效地建立了新型的轻量级异构边缘智能，能够在能源稀缺的环境中自主处理各种数据和计算密集型任务，标志着物联网应用朝着高效的未来迈出了重要一步。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。

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.