Collaborative Research: Integrated Sensing and Normally-off Computing for Edge Imaging Systems

合作研究：边缘成像系统的集成传感和常断计算

基本信息

批准号：
2216773
负责人：
Arman Roohi
金额：
$ 26.07万
依托单位：
University of Nebraska-Lincoln
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216773&HistoricalAwards=false
关键词：
Collaborative Research Integrated Sensing Normally

项目摘要

Internet of Things (IoT) devices are projected to exceed $1000B by 2025, with a web of interconnection projected to comprise approximately 75+ billion IoT devices. The large number of IoTs consists of sensory imaging systems that enable massive data collection from the environment and people. However, considerable portions of the captured sensory data are redundant and unstructured. Data conversion of such large raw data, storing in volatile memories, transmission, and computation in on-/off-chip processors, impose high energy consumption, latency, and a memory bottleneck at the edge. Moreover, because renewing batteries for IoT devices is very costly and sometimes impracticable, energy harvesting devices with ambient energy sources and low maintenance have impacted a wide range of IoT applications such as wearable devices, smart cities, and the intelligent industry. This project explores and designs new high-speed, low-power, and normally-off computing architectures for resource-limited sensory nodes by exploiting cross-layer post-CMOS approaches to overcome these issues. Successful completion of this research will have benefits to a variety of critical application domains, including medical monitoring, industrial and/or environmental sensors. This project will make a strong effort on developing undergraduate and graduate course modules, propagating transportable and open-source models, and broadening STEM participation through publications/presentations at conferences for knowledge dissemination.This project will follow two main research thrusts. Thrust 1 designs and analyzes a Processing-In-Sensor Unit (PISU) co-integrating always-on sensing and processing capabilities in conjunction with a Processing-Near-Sensor Unit (PNSU). The hybrid platform will feature real-time programmable granularity-configurable arithmetic operations to balance the accuracy, speed, and power-efficiency trade-offs under both continuous and energy-harvesting-powered imaging scenarios. This platform will enable resource-limited edge devices to locally perform data and compute-intensive applications such as machine learning tasks while consuming much less power than present state-of-the-art technology. The power profile of ambient energy sources imposes fundamental constraints on processing stability and duration. To achieve high sensing and computation parallelism under unstable power supply conditions, Intermittent-Robust Integrated Sensing Computation (IRISC) will be designed. During power failure, IRISC stores intermediate values in non-volatile spin-based devices, which will ensure uninterrupted operations. To meet the hardware constraints and mitigate the high write power of spin-based devices, they will be selectively and efficiently inserted within the datapaths through a novel NV-clustering methodology to create corresponding intermittent-robust IP cores that realize intermittent computation with lower power consumption while maintaining middleware coherence. This cross-layer devices-to-system research approach will be assessed by developing a comprehensive evaluation framework, a transportable energy-harvested computational workload suite, and FPGA-MRAM-based emulation platforms for IRISC.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.

到2025年，物联网（IoT）设备的设备预计将超过$ 1000B，预计互连的网络预计将构成约75亿多个IoT设备。大量的IoT由感官成像系统组成，这些系统可以从环境和人员那里收集大量数据。但是，捕获的感官数据的大部分是多余的和非结构化的。如此大的原始数据的数据转换，存储在挥发性记忆，传输和计算中，在芯片处理器中，在边缘施加了高能量消耗，潜伏期和内存瓶颈。此外，由于针对物联网设备的更新电池非常昂贵，有时是不切实际的，因此具有环境能源和低维护的能源收集设备影响了广泛的物联网应用，例如可穿戴设备，智能城市和智能行业。该项目通过利用跨层次的频率后方法来克服这些问题，探索并设计了新的高速，低功率和正常计算体系结构，以实现资源有限的感官节点。这项研究的成功完成将对各种关键应用领域，包括医疗监测，工业和/或环境传感器有好处。该项目将在开发本科和研究生课程模块，传播可运输和开源模型，并通过在知识传播会议上通过出版/演示来扩大STEM参与的努力。该项目将遵循两个主要的研究推力。推力1设计并分析一个传感器单元（PISU）与处理 - 连续传感器单元（PNSU）结合的始终接合感应和处理能力。混合动力平台将采用实时可编程的粒度算术算术操作，以平衡连续和能量供电的成像方案的准确性，速度和发电效率的权衡。该平台将使资源有限的边缘设备能够在本地执行数据和计算密集型应用程序，例如机器学习任务，同时消耗的功率要比当前的最新技术要少得多。环境能源的功率概况对处理稳定性和持续时间施加了基本限制。为了在不稳定的电源条件下实现高传感和计算并行性，将设计间歇性的集成感应计算（IRISC）。在功率故障期间，Irisc将中间值存储在非挥发性自旋设备中，这将确保不间断的操作。为了满足硬件约束并减轻基于旋转的设备的高写入能力，通过一种新型的NV群集群集方法，它们将被选择性地，有效地插入数据管，以创建相应的间歇性IP核心，以实现间歇性计算，同时使用较低的动力消耗，同时维持中型软件相干性。这种跨层设备对系统研究方法将通过开发全面的评估框架，可运输的能源收获的计算工作负载套件以及基于FPGA-MRAM的基于FPGA-MRAM的仿真平台进行评估。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识优点和广泛的crietia来评估，并被认为是值得通过评估的支持。