E2CDA: Type I: Collaborative Research: 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许多个数量级。这样做将使这种机器在社会上得到广泛采用，为制造业、医疗保健、农业和许多其他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个人行为趋势的机器可能能够设计出最合适的药物。同样，人们可以设想机器可以学习天气趋势，从而帮助预测下一个作物周期的最佳准备。可能性是无限的。然而，今天的经典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。这个项目的目标是探索、评估和创新新的硬件和软件范例，这些范例可以显著减少学习机的能量消耗。研究团队由数学、神经科学、电子器件和材料、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提供一个独特的平台。我们正在见证计算范式的重大转变。在大量的应用中，分类、识别、综合、决策和学习等认知功能在一个充满传感模式的世界中变得越来越重要，这些模式通常被解释为“大数据”的共同术语，迫切需要有效的信息提取。这与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形成鲜明对比。我们的目标是通过利用认知模型来解决这个问题，这些模型在“一次”学习中显示出有效性。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先确定的数学运算，以确保这些运算后的结果向量是唯一的。唯一性反过来又可以被用作“学习”，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对时，这种“一次性”学习可以显著减少必要的计算运算次数，从而使能量消耗降低数量级。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）。

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).