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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）设备预计将超过10000亿美元，预计互连网络将包括约750多亿个IoT设备。大量的物联网由感官成像系统组成，这些系统能够从环境和人那里收集大量数据。然而，捕获的传感数据的相当大的部分是冗余的和非结构化的。如此大的原始数据的数据转换、存储在易失性存储器中、传输和在片上/片外处理器中的计算在边缘处强加高能耗、延迟和存储器瓶颈。此外，由于更新物联网设备的电池成本非常高，有时甚至不切实际，因此具有环境能源和低维护的能量收集设备已经影响了广泛的物联网应用，如可穿戴设备，智能城市和智能工业。该项目通过利用跨层后CMOS方法来克服这些问题，为资源有限的传感节点探索和设计新的高速，低功耗和常关计算架构。这项研究的成功完成将有利于各种关键应用领域，包括医疗监测，工业和/或环境传感器。本项目将大力开发本科生和研究生课程模块，推广可移植和开源模型，并通过出版物/在知识传播会议上的演讲扩大STEM参与。本项目将遵循两个主要研究方向。推力1设计和分析了传感器内处理单元（PISU），该单元将始终在线的传感和处理能力与近传感器处理单元（PNSU）结合起来。该混合平台将具有实时可编程粒度可配置的算术运算，以平衡连续和能量采集供电成像场景下的准确性，速度和能效权衡。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，如机器学习任务，同时消耗的功率比目前最先进的技术少得多。环境能量源的功率分布对处理稳定性和持续时间施加了基本约束。为了在不稳定的电源条件下实现高感测和计算并行性，将设计间歇鲁棒集成感测计算（IRISC）。在电源故障期间，IRISC将中间值存储在非易失性自旋器件中，这将确保不间断的操作。为了满足硬件约束并减轻基于自旋的设备的高写入功率，它们将通过新颖的NV集群方法选择性地且高效地插入数据路径中，以创建相应的容错性鲁棒的IP核，该IP核在保持中间件一致性的同时以较低的功耗实现间歇计算。这种跨层的设备到系统的研究方法将通过开发一个全面的评估框架，一个可移动的能量收集计算工作负载套件，和基于FPGA的仿真平台IRISC.This奖项反映了NSF的法定使命，并已被认为是值得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的支持。