E2CDA: Type I: Collaborative Research: Energy-efficient analog computing with emerging memory devices

E2CDA：类型 I：协作研究：使用新兴存储设备的节能模拟计算

• 批准号: 1740248
• 负责人: Qiangfei Xia
• 金额: $ 32.1万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740248&HistoricalAwards=false
• 关键词: E2CDA; Type; Collaborative; Research; Energy

The main goal of this project is to develop analog computing circuits that will greatly exceed their digital counterparts in energy-efficiency, speed, and density by employing emerging nonvolatile memory devices. Though analog circuits have been around for a long time, their applications in computing have been rather limited, largely due to the lack of efficient implementations of analog weights. This impediment could be overcome now due to the rapid progress in the emerging nonvolatile memory devices, such as metal-oxide memristors, which are the focus in this project. The analog memory functionality of memristors, combined with high retention and sub-10-nm scaling prospects, might for the first time enable extremely fast and energy-efficient analog implementations of many core operations, such as vector-by-matrix multiplication, which are central to many existing and emerging future applications such as internet-of-the-things and sensor networks, robotics, and energy efficient neuromorphic systems. The results of the proposed research will be integrated into educational curriculum and will help to train material science and electrical engineering students of all levels in this exciting field.The main caveat of the considered analog circuits is their limited operation accuracy, primarily due to the noise and variability in memory devices. The mitigation of this challenge by several means will be one of the main focuses of the project, and will be addressed with highly-interconnected research effort across device, circuit, and architectural layers. At the device level, detailed electrical characterization of analog operation and ways to improve it via material engineering, optimization of electrical stress, and development of efficient tuning algorithms to cope with device variations will be explored. Guided by experimentally-verified device models, the design of several representative analog computing circuits will be optimized. Circuit modeling tools will be developed to capture rich design trade offs in area, speed, energy efficiency, and precision, calibrated on experimental results from wafer-scale integrated memristor circuits, and used for detailed comparison with state-of-the-art digital counterparts. Finally, accurate circuit models will guide exploration of circuit architectures that mitigate limitations of analog computing and assist with detailed system level simulations.

该项目的主要目标是开发模拟计算电路，这些电路将通过采用新出现的非挥发性存储器设备来大大超过其能源效率，速度和密度的数字同行。尽管模拟电路已经存在很长时间了，但它们在计算中的应用非常有限，这在很大程度上是由于缺乏有效的模拟权重实现。由于新出现的非易失性存储器设备（例如金属氧化物回忆录）的快速进步，现在可以克服这种障碍，这是该项目的重点。回忆录的模拟记忆功能，结合高保留率和低于10-nm的缩放前景，可能首次实现了许多核心操作的极快，能节能的模拟实现，例如逐马乘法，这对于许多现有的现有和出现的未来应用程序，例如Internet-Internet-the-TheSthings和Sensor网络和能源效率，这是许多现有和新兴的应用程序，且能源效率效率效率。 拟议的研究的结果将集成到教育课程中，并将有助于在这个令人兴奋的领域中培训各个层次的材料科学和电气工程专业的学生。所考虑的模拟电路的主要警告是它们的操作准确性有限，主要是由于内存设备的噪声和可变性。通过几种方式缓解这一挑战将是该项目的主要重点之一，并将通过设备，电路和建筑层进行高度联系的研究工作来解决。在设备级别上，将探索模拟操作的详细电气表征以及通过材料工程，优化电应力以及开发有效调整算法以应对设备变化的详细电气表征。在实验验证的设备模型的指导下，将优化几个代表性模拟计算电路的设计。将开发电路建模工具，以捕获在晶圆尺度集成的备忘录电路的实验结果上校准的领域，速度，能效和精确度的丰富设计交易折扣，并用于与最先进的数字对应物进行详细比较。最后，准确的电路模型将指导探索电路架构，以减轻模拟计算的限制并帮助进行详细的系统级模拟。

项目成果

Artificial neural networks based on memristive devices

• DOI: 10.1007/s11432-018-9425-1
• 发表时间: 2018-06-01
• 期刊: SCIENCE CHINA-INFORMATION SCIENCES
• 影响因子: 8.8
• 作者: Ravichandran, Vignesh;Li, Can;Xia, Qiangfei
• 通讯作者: Xia, Qiangfei