I-Corps: Hardware Accelerators for Real-Time Decision Making at the Edge

I-Corps项目更广泛的影响/商业潜力是开发一个计算平台，用于解决边缘的非线性优化工作负载。非线性优化是许多关键和新兴技术的基础，例如自动驾驶汽车通过繁忙的十字路口，电网中间歇性可再生能源的最佳定价，或预测制造设备的过早故障。然而，随着技术变得分布式和去中心化，越来越需要在“边缘”执行这些优化—即与生成数据并需要控制或优化的物理设备共存，并且通常在低功耗嵌入式计算硬件上执行，而不是在强大的云数据中心。此外，现有的边缘设备通常不具备执行这些密集计算工作负载的能力，这些工作负载通常由复杂的基于非线性优化的过程组成。所提出的技术旨在将计算速度和能源效率提高一个数量级，以解决复杂的非线性优化问题和高阶偏微分方程。这可能会使多个行业受益，包括运输、制造业、消费电子和能源，并可能使资本和运营费用减少50%以上。此外，通过最大限度地减少将数据从生成数据的边缘移动到目前通常处理工作负载的集中式数据中心的需求，可以减少基础设施和能源成本。这个I-Corps项目是基于硬件加速器的开发，它将计算速度和能源效率提高一个数量级，用于解决复杂的非线性优化问题和高阶偏微分方程。硬件加速器使用混合信号计算技术进行控制和优化，被称为模拟神经计算（ANC），这是一个混合计算平台，利用电子模拟计算技术来解决非线性优化和偏微分方程工作负载，比现有的嵌入式计算平台更快、更有效。在处理某些非线性优化工作负载方面，所提出的技术已被证明比现有的最先进的嵌入式计算平台更快、更有效。已经开发了软件和硬件技术，以最大限度地提高所提出的计算方法的准确性、速度和可用性。非线性优化是有效控制和监测各种重要工业过程的关键技术。最近的进展已经证明了使用这些混合信号计算技术获得稳健和准确解决方案的可行性，这些技术自然会受到一些不希望的变化和现象的影响，例如噪声、工作点依赖性和制造变化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a computing platform for solving nonlinear optimization workloads at the edge. Nonlinear optimization is a foundational aspect of many critical and emerging technologies such as routing autonomous vehicles through a busy intersection, optimally pricing intermittent renewable energy on the grid, or predicting premature failure of manufacturing equipment. However, as technologies become distributed and decentralized, there is a growing need for these optimizations to be performed at the "edge" — that is, co-located with the physical device that is generating data and needs to be controlled or optimized, and typically on low-power embedded computing hardware, as opposed to a powerful cloud data center. In addition, existing edge devices typically do not have the capabilities to perform these intensive computing workloads, often comprised of complex nonlinear optimization-based processes. The proposed technology is designed to increase computational speeds and energy efficiency by an order-of-magnitude for solving complex nonlinear optimization problems and high-order partial differential equations. This may benefit multiple industries, including transportation, manufacturing, consumer electronics, and energy, and could enable reductions in both capital and operating expenses by more than fifty percent. Moreover, reductions in infrastructure and energy costs may be possible by minimizing the need to move data from the edge, where the data is generated, to centralized data centers, where workloads are typically processed today.This I-Corps project is based on the development of hardware accelerators that increase computational speeds and energy efficiency by an order-of-magnitude for solving complex nonlinear optimization problems and high-order partial differential equations. The hardware accelerator uses mixed-signal computing techniques for control and optimization and is referred to as Analog Neural Computing (ANC), which is a hybrid computing platform that leverages electronic analog computing techniques to solve nonlinear optimization and partial-differential equation workloads substantially faster and more efficiently than existing embedded computing platforms. The proposed technology has been demonstrated in handling certain nonlinear optimization workloads faster and more efficiently than existing state-of-the-art embedded computing platforms. Software and hardware techniques have been developed to maximize the accuracy, speed, and usability of the proposed computing approach. Nonlinear optimization is a crucial technology for efficiently controlling and monitoring a variety of important industrial processes. Recent progress has demonstrated the feasibility of obtaining robust and accurate solutions using these mixed-signal computing techniques, which are naturally subject to several undesired variations and phenomena, such as noise, operating point dependencies, and manufacturing variations.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.