Integrated Signal Processing Systems for the Internet-of-Things

物联网集成信号处理系统

• 批准号: RGPIN-2015-05923
• 负责人: Gross, Warren
• 金额: $ 3.42万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689506
• 关键词: Integrated; Signal; Processing; Systems; Internet

The Internet-of-Things (IoT) is the interconnection on a massive scale of autonomous smart objects, enabling innovative applications in health, infrastructure, environment, agriculture, and industry. The goal of the proposed research program is to develop signal processing hardware for smart objects in the IoT addressing two critical challenges: energy efficiency and security. We will investigate fundamentally new approaches addressing the very tight cost, size, and power budgets of smart objects in the IoT that cannot be met by conventional architectures and methodologies by developing neuromorphic ("brain-like") low-power signal processing hardware. The proposed methodology is organized around three research thrusts: i) stochastic computing for low-power signal processing in smart objects, ii) associative memory for secure network processors in the IoT, and iii) polar codes for secure, low-complexity wireless IoT communications. Stochastic computing is the design of hardware that computes with random pulse trains, resulting in remarkably simple logic circuits that offer a natural way to trade-off power consumption with the precision of a computation. Another brain-inspired structure, associative memory, integrates memory with searching and maps naturally to very efficient logic-in-memory architectures. Both of these techniques are inherently fault-tolerant and therefore able to gracefully accommodate errors induced when reducing the supply voltage to minimize power. Polar codes are a breakthrough capacity-achieving error-correction technique for digital communications that can provide secure wireless communications, with a low-complexity decoding algorithm. This work has the potential to introduce a disruptive paradigm for low-power signal processing systems design. In the short term, our approach has the potential to greatly reduce power consumption of critical signal processing components for smart objects in the IoT, demonstrate the advantages of polar codes, driving them towards standards, and train highly qualified personnel to be employed in Canadian industry. In the long term our work will help demonstrate the potential of neuromorphic architectures for emerging computational problems.

物联网（IoT）是大规模自主智能对象的互联，可在健康、基础设施、环境、农业和工业等领域实现创新应用。拟议研究计划的目标是为物联网中的智能对象开发信号处理硬件，解决两个关键挑战：能源效率和安全性。我们将从根本上研究解决物联网智能对象非常紧张的成本、尺寸和功耗预算的新方法，这些方法无法通过开发神经形态（“类脑”）低功耗信号处理硬件的传统架构和方法来满足。所提出的方法围绕三个研究重点进行组织：i)智能对象中低功耗信号处理的随机计算，ii)物联网中安全网络处理器的关联存储器，以及iii)安全，低复杂性无线物联网通信的极性编码。随机计算是一种硬件设计，它使用随机脉冲序列进行计算，从而产生非常简单的逻辑电路，提供了一种自然的方法来权衡功耗和计算精度。另一种受大脑启发的结构是联想记忆，它将记忆与搜索结合起来，并自然地映射到非常高效的内存逻辑架构。这两种技术都具有固有的容错性，因此能够很好地适应在降低电源电压以最小化功率时引起的错误。Polar码是一种突破性的容量纠错技术，用于数字通信，可以提供安全的无线通信，具有低复杂度的解码算法。这项工作有可能为低功耗信号处理系统设计引入一种颠覆性的范例。在短期内，我们的方法有可能大大降低物联网中智能对象的关键信号处理组件的功耗，展示极性代码的优势，推动它们走向标准，并培养高素质的人员在加拿大工业中就业。从长远来看，我们的工作将有助于展示神经形态架构在新兴计算问题上的潜力。