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).

该项目将旨在开发计算硬件和软件，以通过许多数量级来提高学习机的能源效率。这样一来，它将能够大量的社会采用此类机器，为制造业，医疗保健，农业等不同地区的新应用铺平道路。例如，通过从无数传感器中收集数据来学习人类行为趋势的机器可能能够设计最合适的药物。同样，可以设想机器，这些机器学习天气的趋势，从而有助于预测下一个作物周期最优化的准备。可能性实际上是无尽的。但是，当今的规范学习机需要大量的能量，从而极大地阻碍了它们对广泛应用的采用。该项目的目的是探索，评估和创新新的硬件和软件范式，这些范式可以大量减少学习机中的能量消散。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专家组成，这些专家将促进研究生的创新研究和跨学科培训的独特平台。对于大量应用，在一个注入感应方式的世界中，诸如分类，识别，综合，决策和学习之类的认知功能正变得迅速，通常在“大数据”的共同术语下进行了解释，这些术语至关重要，这些术语至关重要，这对于有效的信息吸收至关重要。当计算的核心目的是对数量进行计算并以极高的数值准确性产生结果时，这与过去形成鲜明对比。我们的目标是通过利用在“一击”学习中显示功效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）向量表示。这些向量遵循一组预定的数学操作，这些数学操作确保了此类操作后所产生的向量是唯一的。唯一性又可以用作“学习”，而数学操作的预定性质使学习“一枪”。当与传统的人工神经网络或深度学习算法配对时，这种“一击”学习可以大大减少必要的计算操作的数量，从而导致能量消散的数量级减少。我们将探索整个计算机层次结构，从材料和设备凝视，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