STTR Phase I: Enhancing IoT's Connected Device Capabilities using High-Performance Low-Power RRAM-based FPGAs

STTR 第一阶段：使用基于 RRAM 的高性能低功耗 FPGA 增强物联网的连接设备功能

• 批准号: 1843216
• 负责人: Ali Erdengiz
• 金额: $ 22.5万
• 依托单位: ReRouting, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1843216&HistoricalAwards=false
• 关键词: STTR; Phase; Enhancing; IoT; Connected

The broader impact/commercial potential of this Small Technology Transfer Research (STTR) Phase I project lays in its ability to enable reconfigurable hardware acceleration in the Internet-of-Things (IoT). Users under constrained power at the edge will be able to choose a new solution that can bring acceleration, and enable datacenter like capabilities, and benefit from the IoT's long-sought promise. Using Resistive Random-Access Memory (RRAM) to develop a next-generation Field Programmable Gate Array (FPGA), increasing performance while reducing energy consumption at the sensor node level, is possible. As already experienced in our data driven world, it is critical that we improve our computing capabilities at the edge in order to gain in responsiveness and increase our energy efficiency. With evermore data and performance requirements to deliver on the consumer demands, innovative uses of emerging memories are showing great promise and providing capabilities that will help fulfill the IoT's potential. If fulfilled, this technology has the potential to enable a whole set of data driven applications at the edge, such as low-energy image recognition and learning in drones to operate longer and more effectively or long-lasting medical implants with leading data aggregation and reactiveness.This Small Technology Transfer Research (STTR) Phase I project will aim to commercialize a patented technology to realize a ultra-low-power Field Programmable Gate Array (FPGA) based on Resistive Random-Access Memory (RRAM). To handle the data explosion in Internet of Things (IoT) network, the industry is moving towards increasing intelligent analysis capability for single IoT devices. Tight power budget has become a critical road block: high-end solutions, such as multicore CPUs, GPUs, can provide enough computing capability but fail to meet the power budget, while low-power commercial products, such as micro-controllers and low-power FPGAs, can satisfy power constraints but hardly follow the increasing complexity in data analysis algorithms. This project aims to develop a ultra-low-power FPGA that can offer high-performance data analysis capability under IoT-level power limits. This project will prototype a FPGA chip built around an innovative RRAM-based routing multiplexer design. We will also release an associated software tool suite to support the implementation of customer's applications on the technology. Compared to existing commercial solutions, the proposed FPGA product is expected to demonstrate similar computing capability as high-end FPGA solutions while satisfying an IoT power budget (1W).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.

该小型技术转移研究 (STTR) 第一阶段项目的更广泛影响/商业潜力在于其能够在物联网 (IoT) 中实现可重新配置的硬件加速。边缘电源受限的用户将能够选择一种新的解决方案，该解决方案可以带来加速，并启用类似数据中心的功能，并从物联网的长期追求的承诺中受益。 使用电阻式随机存取存储器 (RRAM) 开发下一代现场可编程门阵列 (FPGA)，可以在提高性能的同时降低传感器节点级别的能耗。正如我们在数据驱动的世界中所经历的那样，为了提高响应能力并提高能源效率，提高边缘计算能力至关重要。为了满足消费者的需求，对数据和性能的要求越来越高，新兴存储器的创新用途显示出巨大的前景，并提供有助于发挥物联网潜力的功能。如果实现，该技术有潜力实现一整套数据驱动的边缘应用，例如无人机中的低能耗图像识别和学习，以便更长时间、更有效地运行，或者具有领先的数据聚合和反应能力的持久医疗植入物。这个小型技术转移研究（STTR）第一阶段项目将旨在将一项专利技术商业化，以实现基于电阻式的超低功耗现场可编程门阵列（FPGA） 随机存取存储器 (RRAM)。为了应对物联网（IoT）网络中的数据爆炸，业界正在努力提高单个物联网设备的智能分析能力。紧张的功耗预算已成为关键的障碍：高端解决方案，如多核CPU、GPU，可以提供足够的计算能力，但无法满足功耗预算，而低功耗商业产品，如微控制器和低功耗FPGA，可以满足功耗限制，但很难跟上数据分析算法日益复杂的要求。该项目旨在开发一款超低功耗FPGA，能够在物联网级功耗限制下提供高性能数据分析能力。该项目将围绕基于 RRAM 的创新路由多路复用器设计构建 FPGA 芯片原型。我们还将发布相关的软件工具套件，以支持客户在该技术上实施应用程序。与现有的商业解决方案相比，拟议的 FPGA 产品预计将展示与高端 FPGA 解决方案类似的计算能力，同时满足物联网功耗预算 (1W)。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。