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

该项目旨在开发计算硬件和软件，将学习机器的能源效率提高许多数量级。通过这样做，它将使这种机器的大规模社会采用成为可能，为制造业、医疗保健、农业等许多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学习人类个体行为趋势的机器可能能够设计出最合适的药物。类似地，人们可以设想机器学习天气趋势，从而帮助预测下一个作物周期的最优化准备。可能性简直是无穷无尽的。然而，今天的规范学习机器需要大量的能量，这大大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，以显著降低学习机器的能耗。研究团队由数学、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提供一个独特的平台。我们正在见证计算范式的军团式转变。对于大量的应用程序，认知功能，如分类，识别，综合，决策和学习正在获得快速的重要性，在一个充满了感测模式的世界，通常在一个共同的术语下解释为“大数据”，这是迫切需要有效的信息提取。这与过去形成了鲜明的对比，当时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”学习中表现出功效的认知模型来解决这个问题。在这种方法中，信息是由高维（HD）向量的手段。这些向量遵循一组预定的数学运算，这些运算确保在这些运算之后得到的向量是唯一的。这种唯一性可以反过来被用作“学习”，而数学运算的预定义性质使得学习“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操作的数量，从而导致能量耗散的数量级减少。我们将探索整个计算机层次结构，从材料和设备开始，一直到系统设计和优化，以利用HD计算提供的独特功能，最终目标是实现节能学习机器（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).