E2CDA: Type I: Collaborative Research: Energy Efficient Learning Machines (ENIGMA)

E2CDA：类型 I：协作研究：节能学习机 (ENIGMA)

• 批准号: 1640078
• 负责人: Subhasish Mitra
• 金额: $ 67.85万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640078&HistoricalAwards=false
• 关键词: E2CDA; Type; Collaborative; Research; Energy

The project will aim to develop computing hardware and software that improve the energy efficiency of learning machines by many orders of magnitude. In doing so it will enable large societal adoption of such machines, paving the way for new applications in diverse areas such as manufacturing, healthcare, agriculture, and many others. For example, machines that learn the behavioral trends of individual human beings by collecting data from myriads of sensors may be able to design the most appropriate drugs. Similarly, one may envision machines that learn trends in the weather and thereby assist in predicting the most optimized preparations for the next crop cycle. The possibilities are literally endless. However, the canonical learning machines of today need huge amount of energy, significantly hindering their adoption for widespread applications. The goal of this project will be to explore, evaluate and innovate new hardware and software paradigms that could reduce energy dissipation in learning machines by a significant amount. The team of researchers consists of experts in mathematics, neuroscience, electronic devices and materials and computer circuit and system design that will foster a unique platform for both innovative research and interdisciplinary training of graduate students.We are witnessing a regimental shift in the computing paradigm. For a vast number of applications, cognitive functions such as classification, recognition, synthesis, decision-making and learning are gaining rapid importance in a world that is infused with sensing modalities, often paraphrased under a common term of "Big Data", that are in critical need of efficient information-extraction. This is in sharp contrast to the past when the central objective of computing was to perform calculations on numbers and produce results with extreme numerical accuracy. We aim to approach this problem by exploiting cognitive models that have shown efficacy in "one shot" learning. In this approach, the information is represented by means of high dimensional (HD) vectors. These vectors follow a set of predetermined mathematical operations that ensure that the resulting vector after such operations is unique. The uniqueness can in turn be used as "learning" and the predefined nature of mathematical operations make the learning "one shot". When paired with traditional artificial neural network or deep learning algorithms, such "one shot" learning could significantly reduce the number of necessary computing operations, leading to orders of magnitude reduction in energy dissipation. We shall explore the entire computer hierarchy, staring from materials and devices, all the way up to system design and optimization to exploit the unique capabilities afforded by the HD computing, with the ultimate objective of realizing energy efficient learning machines (ENIGMA).

该项目旨在开发计算硬件和软件，将学习机的能源效率提高多个数量级。这样做将使此类机器在社会上得到广泛采用，为制造、医疗保健、农业等不同领域的新应用铺平道路。例如，通过从无数传感器收集数据来了解人类个体行为趋势的机器也许能够设计出最合适的药物。同样，人们可能会设想机器能够学习天气趋势，从而帮助预测下一个作物周期的最优化准备工作。可能性实际上是无限的。然而，当今的规范学习机器需要大量的能量，这极大地阻碍了它们的广泛应用。该项目的目标是探索、评估和创新新的硬件和软件范例，这些范例可以显着减少学习机的能量耗散。研究人员团队由数学、神经科学、电子设备和材料以及计算机电路和系统设计方面的专家组成，将为研究生的创新研究和跨学科培训打造一个独特的平台。我们正在见证计算范式的全面转变。对于大量应用来说，分类、识别、综合、决策和学习等认知功能在充满传感模式的世界中变得越来越重要，这些模式通常用“大数据”这一通用术语来解释，即迫切需要有效的信息提取。这与过去形成鲜明对比，过去计算的中心目标是对数字进行计算并产生具有极高数值精度的结果。我们的目标是通过利用在“一次性”学习中显示出有效性的认知模型来解决这个问题。在这种方法中，信息通过高维（HD）向量来表示。这些向量遵循一组预定的数学运算，以确保这些运算后得到的向量是唯一的。这种独特性又可以用作“学习”，并且数学运算的预定义性质使学习成为“一次性”。当与传统的人工神经网络或深度学习算法配合使用时，这种“一次性”学习可以显着减少必要的计算操作数量，从而使能量耗散减少几个数量级。我们将探索整个计算机层次结构，从材料和设备开始，一直到系统设计和优化，以利用高清计算提供的独特功能，最终目标是实现节能学习机（ENIGMA）。

项目成果

Illusion of large on-chip memory by networked computing chips for neural network inference

• DOI: 10.1038/s41928-020-00515-3
• 发表时间: 2021-01
• 期刊: Nature Electronics
• 影响因子: 34.3
• 作者: R. Radway;Andrew Bartolo;Paul C. Jolly;Zainab F. Khan;B. Le;Pulkit Tandon;Tony F. Wu;Yunfeng Xin;E. Vianello;P. Vivet;E. Nowak;H. Wong;M. Aly;E. Beigné;Mary Wootters;S. Mitra

Brain-inspired computing exploiting carbon nanotube FETs and resistive RAM: Hyperdimensional computing case study

• DOI: 10.1109/isscc.2018.8310399
• 发表时间: 2018-02
• 期刊: 2018 IEEE International Solid - State Circuits Conference - (ISSCC)
• 作者: Tony F. Wu;Haitong Li;Ping-Chen Huang;Abbas Rahimi;J. Rabaey;H. Wong;M. Shulaker;S. Mitra